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Glama

Figshare

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds valuable details: it returns passages with character offsets and similarity scores, enables verbatim verification, and discloses the truncation cap at 200K chars. It also explains the embedding model and windowing, which goes beyond 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 but each sentence adds value: purpose, use case, pairing, and technical limits. It is front-loaded with the core action and only includes necessary technical details. Slightly long but not wasteful.

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?

With no output schema, the description covers return format (passages with offsets and similarity scores), input constraints (200K char truncation), and usage context. It also mentions the pairing with ask_pipeworx_grounded, making it complete for an AI agent to select and invoke correctly.

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 coverage is 100%, so the baseline is 3. The description reinforces the 'text' as already-pulled record and 'query' as natural-language, but adds little beyond the schema's own descriptions. It does mention top-N passages which aligns with limit.

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 opens with 'Semantic search INSIDE a fetched record,' a specific verb and resource. It clearly distinguishes from siblings by emphasizing 'the text you already pulled' and 'Use when the record is too big to cram into the prompt,' differentiating it from ask_pipeworx and grounded tools.

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?

It explicitly states when to use: 'Use when the record is too big to cram into the prompt.' It also names the alternative workflow: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This gives clear context and alternative.

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

Multiple tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are documented as currently identical, ask_pipeworx_grounded/deep_research route the same class of questions, and the six polymarket_* tools plus bet_research form a confusing cluster. Some clusters (Figshare fetch/search, memory, subscriptions) are distinct, but overall boundaries are frequently unclear.

Naming Consistency4/5

Names are uniformly lowercase snake_case and mostly follow a predictable verb_noun pattern (ask_pipeworx, list_subscriptions, resolve_entity, scan_dependency). The main inconsistency is the mix of bare-noun Figshare resource names (article, articles, collection, collections) with verb-phrase tool names.

Tool Count2/5

38 tools is well above the comfortable scope for a coherent server, especially one named Figshare. Most of the surface is unrelated to Figshare, bundling Pipeworx research, Polymarket analysis, memory, subscriptions, AI visibility, and npm checks into a single connection.

Completeness2/5

The Figshare read-side is reasonably covered (search, article metadata, files, collections, categories, licenses), but there are no create/update/delete or account/upload operations. The broader advertised surface is a grab bag with no clear domain boundary, and several subdomains are shallow while prediction markets are over-represented.

Resources