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Glama

Cryptocompare

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?

Goes beyond annotations by detailing embedding model (BGE-base-en), windowing (500-char overlapping windows), character cap (200K chars with truncation flag), and output features (character offsets for verbatim quotes). Annotations already indicate read-only/idempotent; description adds rich behavioral context.

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?

Well-structured with clear topic sentences. Slightly verbose (120 words) but every sentence adds useful information. Could be trimmed slightly but still effective.

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 lacking output schema, the description fully explains return content (passages with offsets and scores), covers truncation behavior, and includes technical details. Completely sufficient for agent understanding.

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 covers all 3 parameters (100% coverage). Description adds value: explains query as natural-language with examples, mentions default and range for limit, and adds 200K char constraint for text parameter.

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 'Semantic search INSIDE a fetched record' and gives concrete examples (SEC 10-K, article, tool result). It distinguishes the tool from siblings by explaining its pairing with 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 tells when to use: 'when the record is too big to cram into the prompt'. Explains how it pairs with ask_pipeworx_grounded, providing clear guidance on alternatives.

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

B3.1/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx, its beta, and grounded variants, the six polymarket_* tools, and the three price endpoints (price, price_full, price_multi) can be confused despite distinct purposes. Long descriptions provide some disambiguation, but an agent must read carefully to select the correct tool.

Naming Consistency4/5

Tool names mostly follow snake_case with verb_noun or noun_noun patterns (all_coins, compare_entities, top_market_cap), and related families share clear prefixes (histo_*, polymarket_*). Minor deviations exist (bare verbs like remember/forget, brand names like ask_pipeworx), but the overall pattern is readable and consistent.

Tool Count2/5

With 46 tools, the server is far beyond the 3-15 well-scoped range and nearly double the 25-tool threshold for 'too many'. Many tools are unrelated to the server's apparent crypto purpose (generate_llms_txt, scan_dependency, memory helpers), making it feel like a general-purpose utility rather than a focused data service.

Completeness3/5

The crypto data surface is fairly complete (spot, historical, top lists, news, social stats, exchange metadata), and the Pipeworx meta-tools (ask_pipeworx, deep_research, entity_profile) cover a broad range of factual queries. However, there are notable gaps: no direct way to fetch pipeworx:// citation URIs, and no advanced crypto order-book/trade endpoints, leaving some workflows as dead ends.

Resources