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Glama

Cbioportal

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".

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate safe read operation. Description adds specific behavioral details: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).' No contradictions.

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?

Single paragraph packs essential information without fluff. Could benefit from slight structuring (e.g., bullet points) but still efficient and front-loaded with key info.

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?

No output schema, but description covers return values: 'top-N passages with character offsets and similarity scores.' Also explains model details, truncation, and relationship to sibling tool. Complete for a search tool.

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

Parameters4/5

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

Schema description coverage is 100%, baseline 3. Description adds meaning by explaining parameters: 'text' is the document, 'query' is natural-language, 'limit' max passages. Also notes truncation flag behavior.

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?

Description states 'Semantic search INSIDE a fetched record' with specific verb and resource. Examples like 'SEC 10-K body' and distinction from sibling 'ask_pipeworx_grounded' make purpose crystal clear.

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: 'Use when the record is too big to cram into the prompt.' Also pairs with 'ask_pipeworx_grounded' and suggests grounding over passages instead of 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

A3.6/5.0
Disambiguation3/5

Most tools have clearly distinct purposes, but ask_pipeworx_beta is an intentional near-duplicate of ask_pipeworx, and several polymarket/entity tools overlap in scope. The descriptions do enough to disambiguate most pairs, but the duplicate beta routing tool introduces real ambiguity.

Naming Consistency3/5

All names use snake_case, but the pattern varies: verb_noun (get_gene, search_studies), noun_noun (polymarket_edges, pipeworx_trending), and product-prefixed verbs (ask_pipeworx, bet_research). There is no single consistent convention, though the names remain readable.

Tool Count2/5

35 tools is a heavy surface, and the vast majority (31) are unrelated to cBioPortal; only four tools actually belong to the named domain. This makes the count inappropriate for a cancer-genomics MCP server, as the set is bloated with out-of-scope utilities.

Completeness1/5

For a cBioPortal server, only metadata-level tools exist (gene lookup, study details, cancer types, study search); core cBioPortal data access — mutations, copy-number alterations, clinical data, molecular profiles, sample-level queries — is entirely missing. The tool surface severely under-covers the named domain.

Resources