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Glama

Datacite

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare safety hints (readOnlyHint, idempotentHint). The description adds valuable behavioral details: embedding model (BGE-base-en), windowing (500-char overlapping), cap (200K chars with truncation flag), and output structure (offsets and similarity scores). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and informative, but slightly verbose as a single paragraph. It could be broken into shorter sentences for easier parsing. However, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no output schema, and full schema coverage, the description provides complete information: algorithm, constraints, and usage context. The agent will know exactly how and when to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds context beyond field descriptions: embedding algorithm, window size, character limit, and example queries. It explains how the search works internally, which the schema does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs semantic search inside a fetched record, with specific examples (SEC 10-K, article, tool result). It distinguishes itself from siblings like ask_pipeworx_grounded.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use: 'Use when the record is too big to cram into the prompt.' Provides a clear alternative: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation2/5

Several tools occupy overlapping roles: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points (beta currently behaves exactly like stable), while entity_profile, recent_changes, and compare_entities all target company research. The prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) is large enough that an agent could easily select the wrong one.

Naming Consistency2/5

Naming conventions are mixed throughout: verb_noun patterns (search_dois, list_repositories, validate_claim) coexist with noun phrases (entity_profile, recent_alerts, ai_visibility_check) and bare verbs (remember, recall, forget, subscribe). The only consistent thread is snake_case, but the grammatical style is unpredictable.

Tool Count2/5

At 34 tools, the server is overstuffed, especially given its apparent focus is DataCite but only 3 tools actually serve that domain (get_doi, search_dois, list_repositories). The rest are a grab bag of Pipeworx data routing, prediction-market analytics, memory, and subscription utilities that would be better split into separate focused servers.

Completeness2/5

The DataCite surface is incomplete (no DOI creation, update, or deletion despite DataCite supporting registration), while the Pipeworx surface has no direct fetch-by-URI tool for the pipeworx:// citations that other tools return. The presence of a duplicate beta router and a beta router with no active differences further muddies the coverage picture.

Resources