Sanskrit research often requires more than a generic AI summary. A useful system must preserve Devanagari, distinguish the original text from translation, identify uncertain readings, and provide citations that a researcher can verify. A WebMCP tool can expose these capabilities to AI agents through a structured, permission-aware interface.
This guide explains how to create a WebMCP tool for agents to summarize Sanskrit texts for research, from defining the tool contract to implementing text extraction, linguistic preprocessing, model prompting, provenance, evaluation, and production safeguards. The examples use TypeScript-style pseudocode, but the architecture applies to any WebMCP-compatible implementation.
What a WebMCP Sanskrit summarization tool should do
A WebMCP tool is an agent-callable web capability. Instead of asking an agent to scrape a page or invent an API request, you give it a defined operation, input schema, output schema, and policy boundary.
For Sanskrit research, the tool should ideally support:
- Source acquisition: accept pasted text, a stable URL, a document identifier, or selected page content.
- Script preservation: retain Devanagari exactly as supplied and optionally provide IAST or another transliteration.
- Segmentation: divide text by verse, sentence, prose section, or source-defined unit.
- Summary modes: literal synopsis, thematic overview, argument map, or research abstract.
- Evidence tracking: attach source spans, verse numbers, page references, and confidence notes.
- Linguistic context: expose sandhi, compounds, named entities, and uncertain parsing without presenting guesses as facts.
- Research outputs: return structured JSON suitable for citations, note-taking, comparison, or downstream analysis.
The tool should not silently translate difficult passages, normalize variant readings, or collapse a commentary and its base text into one undifferentiated summary.
Define the research workflow before writing code
Start by specifying who will use the tool and what a successful result means. A Sanskrit philologist, historian, Indologist, student, and general reader may require very different outputs.
A reliable workflow can be divided into six stages:
1. Identify the source and verify that the user has permission to process it.
2. Extract and normalize text while retaining the original representation.
3. Segment the document into traceable units.
4. Analyze and summarize using a controlled prompt and selected model.
5. Validate claims against source spans and linguistic metadata.
6. Return an auditable result with provenance, uncertainty, and model information.
This sequence matters. If summarization happens before segmentation and provenance are established, an agent may produce a polished paragraph that cannot be checked against the Sanskrit.
Design a narrow WebMCP tool contract
Avoid one oversized tool such as summarize_any_sanskrit_document. Narrow operations are easier for agents to select, easier to secure, and easier to test.
A useful initial tool set is:
extract_sanskrit_sourcesegment_sanskrit_textsummarize_sanskrit_unitscompare_sanskrit_summariesexplain_sanskrit_claim
If your first release needs only summarization, expose one tool with explicit options rather than dozens of loosely defined parameters.
Example input schema
const summarizeSanskritInput = {
type: "object",
required: ["text", "summaryMode"],
properties: {
text: {
type: "string",
minLength: 1,
maxLength: 120000,
description: "Sanskrit text in Devanagari, IAST, or another declared script"
},
script: {
type: "string",
enum: ["devanagari", "iast", "itrans", "unknown"]
},
title: { type: "string", maxLength: 500 },
sourceUrl: { type: "string", format: "uri" },
citation: { type: "string", maxLength: 1000 },
summaryMode: {
type: "string",
enum: ["literal", "thematic", "argument", "research_abstract"]
},
audience: {
type: "string",
enum: ["beginner", "student", "researcher"]
},
includeTransliteration: { type: "boolean", default: false },
includeTranslation: { type: "boolean", default: false },
unitType: {
type: "string",
enum: ["verse", "sentence", "paragraph", "automatic"]
},
maxSummaryWords: {
type: "integer",
minimum: 50,
maximum: 3000,
default: 500
}
},
additionalProperties: false
};The script, unitType, and summaryMode fields reduce ambiguity. Do not infer a user’s preferred translation style from vague natural-language instructions when a controlled option can be provided.
Example output schema
interface SanskritSummaryResult {
document: {
title?: string;
detectedScript: string;
characterCount: number;
unitCount: number;
source?: { url?: string; citation?: string };
};
summary: string;
keyThemes: string[];
units: Array<{
id: string;
sourceText: string;
transliteration?: string;
translation?: string;
summary: string;
sourceSpan: { start: number; end: number };
confidence: "high" | "medium" | "low";
notes?: string[];
}>;
caveats: string[];
provenance: {
model: string;
promptVersion: string;
generatedAt: string;
inputHash: string;
};
}Returning source spans is particularly important. A researcher should be able to click from a claim in the summary back to the exact verse or sentence that supports it.
Preserve Devanagari and Unicode correctly
Sanskrit processing fails frequently because of text handling rather than model quality. Store the original input as UTF-8 and do not overwrite it with normalized output.
Use two representations:
- Original text: byte-for-byte or character-for-character preservation where possible.
- Processing text: normalized copy used for segmentation, search, and linguistic analysis.
Unicode normalization can affect combining marks and visually equivalent sequences. Keep the normalization form explicit in metadata, commonly NFC for general storage, and test it against real manuscript transcriptions and OCR output.
Also account for:
- Devanagari vowel signs and virāma characters
- zero-width characters introduced by copy-paste
- danda and double danda punctuation (
।,॥) - avagraha (
ऽ) - Vedic accents when present
- mixed-script documents containing Latin citations or editorial notes
- OCR substitutions such as visually similar characters
A practical preprocessing record might include the original hash, normalized hash, detected script, removed-character list, and normalization version. Never silently delete characters that may be philologically meaningful.
Segment verses, sentences, and commentary safely
Sanskrit verse segmentation is not identical to modern sentence segmentation. Dandas may indicate a verse boundary, but editions vary. A prose passage may contain nested quotations, commentary markers, or abbreviations.
Use a layered strategy:
1. Prefer explicit user-provided unit boundaries.
2. Detect danda-based boundaries as candidates.
3. Preserve verse numbering and editorial markers.
4. Use linguistic sentence segmentation only when punctuation and syntax support it.
5. Mark uncertain boundaries instead of pretending they are definitive.
For a śloka, retain the complete unit and, if meter analysis is available, store pāda boundaries separately. For commentaries, label the base text and commentary as separate layers. A summary that attributes a commentator’s interpretation to the root text is a serious research error.
Add Sanskrit-aware analysis without overclaiming
A language model can summarize Sanskrit, but it should not be treated as an infallible parser or translator. Build a pipeline that separates observations from interpretations.
Useful intermediate annotations include:
- tokenization and sandhi split candidates
- lemma and morphological analyses
- compound candidates
- named entities and technical terms
- meter or verse-form indicators
- direct speech and quotation boundaries
- translation alternatives
- uncertain or disputed readings
Each annotation should carry a confidence score and, where possible, the tool or lexicon that produced it. If multiple morphological parses are plausible, return alternatives rather than selecting one without explanation.
For technical terms, create a domain glossary. Terms in Nyāya, Mīmāṃsā, Vedānta, Buddhist Sanskrit, Ayurveda, grammar, and poetics may not map cleanly to one English equivalent. The summary should retain the Sanskrit term and explain the chosen rendering when ambiguity affects the argument.
Build a citation-grounded summarization prompt
The model prompt should force the agent to summarize only from supplied text and attached annotations. A robust pattern is:
You are summarizing Sanskrit source material for academic research.
Rules:
1. Separate what the Sanskrit explicitly states from interpretation.
2. Do not invent missing verses, authorship, dates, manuscript details, or references.
3. Preserve technical Sanskrit terms when translation would lose meaning.
4. Attach every substantive claim to one or more unit IDs.
5. Flag uncertain segmentation, OCR, parsing, and translation.
6. Distinguish root text, commentary, editorial notes, and supplied translation.
7. If the input is insufficient, say so directly.
Output valid JSON matching the provided schema.Pass segmented units with stable IDs such as U001, U002, and U003. Require claim-to-unit mappings in the output, even if the user-facing interface later renders them as footnotes.
Use separate prompts for literal, thematic, and argumentative summaries. A literal synopsis should stay close to the source sequence; a thematic summary may reorganize ideas; an argument map should identify premises, conclusions, objections, and responses only when the text supports those relationships.
Expose the tool to agents through WebMCP
The WebMCP layer should publish clear metadata so agents can discover and select the capability correctly. Include:
- tool name and human-readable description
- input and output schemas
- maximum input size and expected latency
- whether the operation is read-only
- data retention and privacy behavior
- supported scripts and summary modes
- error codes and retry guidance
- citation and provenance guarantees
A conceptual registration might look like this:
registerTool({
name: "summarize_sanskrit_text",
description: "Create a citation-grounded summary of supplied Sanskrit text.",
inputSchema: summarizeSanskritInput,
outputSchema: sanskritSummaryOutput,
annotations: {
readOnly: true,
handlesSensitiveData: false,
requiresUserConfirmation: false
},
handler: async (input, context) => {
const verified = validateInput(input);
const prepared = await prepareSanskrit(verified);
const units = segmentText(prepared);
const result = await generateGroundedSummary(units, verified);
return validateOutput(addProvenance(result, prepared, context));
}
});Exact registration APIs differ between WebMCP implementations. The essential principle is that the tool advertises a stable contract and does not allow an agent to change hidden model settings, access unrelated user data, or fetch arbitrary external pages without permission.
Handle URLs, documents, and copyright responsibly
Pasted text is the simplest and safest input. URL ingestion introduces additional risks: paywalls, robots restrictions, malicious content, unstable pages, and copyright concerns.
If you support URLs:
- fetch only after explicit user instruction
- restrict protocols to HTTPS
- enforce domain and content-size limits
- strip scripts and active content
- record the final URL and retrieval time
- preserve page and section metadata
- respect access controls and publisher terms
- avoid storing full copyrighted texts unless authorized
For scanned books and manuscripts, OCR output should be labelled as OCR. If a page image is available, store page coordinates or image references so users can verify questionable readings. A summary generated from poor OCR must visibly carry a lower confidence level.
Add security, privacy, and abuse controls
Even a read-only summarization tool can be attacked through prompt injection in the source text. A Sanskrit document may contain instructions such as “ignore previous rules” after OCR or transcription. Treat all source text as untrusted data, not as system instructions.
Recommended controls include:
- isolate source content from system and developer messages
- validate and cap input size, recursion, and output length
- escape rendered HTML and prevent script execution
- rate-limit anonymous users
- redact personal data when documents contain modern notes or archives
- log tool calls without unnecessarily storing full texts
- use request IDs and input hashes for reproducibility
- validate model output against a strict JSON schema
- reject unsupported claims and malformed citations
If the tool calls external linguistic APIs or language models, disclose subprocessors and retention policies. Indian research institutions may also require internal data-governance review, especially for unpublished manuscripts, restricted archives, or personal correspondence.
Evaluate the tool with a Sanskrit research benchmark
Generic summary metrics are not enough. Build a small expert-reviewed benchmark covering multiple genres, scripts, lengths, and difficulty levels.
Include samples from areas such as:
- epic and purāṇic narrative
- kāvya and drama
- śāstra and philosophical prose
- Buddhist or Jain Sanskrit
- grammatical and technical texts
- commentarial passages
- OCR-derived and manually transcribed sources
Evaluate at least these dimensions:
- Faithfulness: Does every major claim follow from the source?
- Coverage: Are central propositions and narrative events included?
- Citation accuracy: Do source spans actually support the claim?
- Attribution: Are speakers, authors, commentators, and quoted sources distinguished?
- Terminology: Are technical terms translated consistently and transparently?
- Uncertainty calibration: Does low-quality input produce appropriate caveats?
- Segmentation quality: Are verse and prose units correctly identified?
- Reproducibility: Can another researcher reproduce the result from the recorded version and hash?
Have Sanskrit-qualified reviewers score samples independently. Track error categories, not only average scores. A single fabricated attribution may be more damaging than several stylistic omissions.
Improve cost, latency, and model selection
Long texts can exceed context limits and produce expensive, slow requests. Use hierarchical summarization:
1. segment the source;
2. summarize each unit with citations;
3. summarize groups of unit summaries;
4. generate the final research abstract from the intermediate evidence.
Do not summarize summaries indefinitely; retain the original unit-level evidence throughout the pipeline. Cache preprocessing and deterministic intermediate outputs using an input hash plus tool-version identifier.
For model selection, test at least one multilingual or Indic-capable model against a stronger general model. Compare Sanskrit comprehension, Devanagari handling, technical terminology, and citation discipline rather than relying on English benchmark scores. Keep temperature low for research extraction, and make model and prompt versions visible in the result.
Create a researcher-friendly interface
The agent may call the tool, but researchers need to inspect and correct its work. A good interface should show:
- original Devanagari beside transliteration and translation
- expandable source spans for every summary claim
- confidence and caveat badges
- separate tabs for root text, commentary, and editorial material
- copyable citations with page, verse, or unit identifiers
- export to Markdown, JSON, CSV, or reference-management notes
- a correction workflow for OCR, segmentation, and terminology
Allow users to edit a segmentation or glossary entry and rerun only the affected stage. This is more efficient and more trustworthy than regenerating the entire document after every correction.
Common mistakes to avoid
- Treating translation as ground truth: A supplied translation may be outdated, interpretive, or incorrect.
- Removing diacritics too early: This can damage transliteration-based search and lexical analysis.
- Mixing editions: Never combine verse numbering or readings from different editions without labelling them.
- Hiding uncertainty: “Confidently wrong” is especially dangerous in philological research.
- Using one giant prompt: Modular extraction and summarization are easier to audit.
- Returning prose without evidence: Every substantive research claim needs a source location.
- Ignoring commentary structure: Attribution errors can reverse the meaning of a passage.
- Failing to version prompts: Changes in prompts or models can alter published research notes.
A practical launch checklist
Before releasing the WebMCP tool, confirm that:
- the input and output schemas reject ambiguous or oversized requests;
- original text is preserved separately from normalized text;
- Devanagari, IAST, punctuation, and Vedic marks are tested;
- unit IDs and source spans survive every pipeline stage;
- root text and commentary are represented separately;
- model output is schema-validated and citation-checked;
- OCR and uncertain parses are clearly labelled;
- URL fetching follows access, privacy, and copyright rules;
- tool metadata explains capability and limitations to agents;
- expert reviewers have evaluated representative passages;
- prompt, model, preprocessing, and glossary versions are recorded;
- users can export, inspect, and correct results.
A strong first version does not need to solve all Sanskrit NLP. It needs to be narrow, transparent, and useful: accept a bounded source, produce a structured summary, show the evidence, and state what the system cannot determine.
FAQ
Can a general AI agent summarize Sanskrit without a specialized tool?
It can generate a rough summary, but a WebMCP tool adds controlled inputs, script preservation, segmentation, citation links, provenance, and repeatable research workflows. Those controls are essential when accuracy matters.
Should the tool translate Sanskrit before summarizing it?
Not always. For research, retain the original Sanskrit and treat translation as an optional, separately labelled layer. Summarize from the source units and expose alternative readings or translation uncertainty.
Which Sanskrit script should the tool support first?
Devanagari is a practical starting point, but IAST support is valuable for scholarly workflows. Store the original script and avoid irreversible transliteration or diacritic removal.
How can I reduce hallucinations?
Use citation-grounded prompts, unit-level source spans, strict output validation, low-variance generation settings, expert evaluation, and explicit instructions to abstain when the text or parsing is insufficient.
Is a WebMCP tool suitable for unpublished manuscripts?
Only with appropriate authorization and privacy controls. Use explicit consent, minimal retention, access restrictions, and institutional review where required. Do not send sensitive manuscripts to external services without checking their data policies.
Apply for AI Grants India
If you are an Indian AI founder building a research-grade WebMCP tool for Sanskrit, Indic languages, or scholarly knowledge systems, apply to AI Grants India for support and funding opportunities. Share your technical approach, evaluation plan, and potential impact on Indian research.