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Glama

Pride

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only and non-destructive, but the description goes beyond by disclosing specific mechanics (BGE-base-en embeddings, cosine similarity, 500-char overlapping windows), return format (passages with offsets and scores), and the 200K character cap with truncation flagging. This rich context helps the agent predict behavior and handle edge cases without contradicting annotations.

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

Conciseness5/5

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

The description is dense but well-structured: first sentence establishes the core function, second sentence gives the use case, third provides technical details and limits. Every sentence earns its place, and it is front-loaded with the most important information. Length is appropriate for the tool's complexity.

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?

Despite having no output schema, the description explains return values (passages with character offsets and similarity scores), the algorithm and windowing approach, size caps, and truncation behavior. It also mentions pairing with a sibling tool, providing a complete picture for safe invocation and result interpretation.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the schema's parameter meanings (e.g., 'Pass the text you already pulled' for text, natural-language query for query) without adding substantial new parameter-level semantics beyond what the schema already provides. No parameter is left unexplained.

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 verbs and resource (search inside text). It distinguishes itself from siblings by emphasizing it operates on already-pulled text and even contrasts with ask_pipeworx_grounded, making its niche unambiguous.

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 states when to use: when the record is too big to fit in the prompt, and provides a concrete alternative/pairing strategy with ask_pipeworx_grounded. The phrase 'Pass the text you already pulled' establishes a clear prerequisite, giving the agent actionable guidance.

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

A3.9/5.0
Disambiguation2/5

Several tools are near-identical in purpose: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same style of question, and the company-research tools (entity_profile, compare_entities, recent_changes) and Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) have overlapping triggers. The long descriptions help, but an agent could easily select the wrong one.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb-led (ask_pipeworx, resolve_entity, scan_dependency), some are noun-led (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective-noun phrases (recent_changes, recent_alerts). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set has no unifying pattern.

Tool Count2/5

33 tools is high and the server bundles several unrelated domains: Pipeworx data research, prediction markets, PRIDE proteomics, memory, subscriptions, and AI-visibility checks. Each cluster is individually useful, but the aggregate surface feels over-stuffed rather than well-scoped for a single MCP server.

Completeness4/5

The main clusters are well covered: query/grounded/deep research, entity resolution/profile/compare/validate, memory CRUD, subscription lifecycle, and a rich Polymarket analytics toolkit. Minor gaps exist—PRIDE is limited to metadata search/get and there is no trade execution for prediction markets—but most workflows can be completed without dead ends.

Resources