Skip to main content
Glama

Wikipathways

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

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, openWorldHint), the description adds significant detail: returns top-N passages with character offsets and similarity scores, uses BGE-base-en embeddings + cosine over 500-char overlapping windows, and caps input at 200K chars with truncation flag. 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.

Conciseness5/5

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

Three sentences, front-loaded with the core function, and every sentence adds unique value. No redundant or extraneous information.

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 annotations that are well-supplemented, the description explains how the search works (embeddings, windowing), constraints (character cap), output details (offsets, scores), and usage guidance. It is fully self-contained.

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 coverage is 100%, but the description adds value by explaining the truncation behavior for long inputs, providing example queries, and detailing the output format (passages with offsets and scores). This goes beyond the schema's minimal descriptions.

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 explains the specific use case of searching within large text. It distinguishes itself from siblings by explicitly pairing with ask_pipeworx_grounded and noting it is for when the record is too big for the prompt.

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?

The description explicitly guides usage: 'Use when the record is too big to cram into the prompt' and mentions pairing with ask_pipeworx_grounded. It also provides example queries and notes that every passage carries an offset for verification.

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

All Wikipathways toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.3/5.0
Disambiguation1/5

The tool set is severely mismatched: only 3 of 34 tools (get_pathway, list_pathways, search_pathways) relate to WikiPathways while the rest form overlapping Pipeworx/Polymarket families (ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded; multiple polymarket_* tools) with unclear boundaries and overlapping purposes.

Naming Consistency2/5

Naming conventions are highly inconsistent: product-specific names (pipeworx_feedback, pipeworx_trending), generic memory verbs (remember, recall, forget), and mixed snake_case patterns with no unifying verb_noun structure. The names do not reflect the WikiPathways domain at all.

Tool Count1/5

34 tools is far too many for a WikiPathways server, which only needs a handful of pathway-related operations. Nearly all tools belong to unrelated domains (SEC, FDA, Polymarket, npm, etc.), making the set feel bloated and unfocused.

Completeness1/5

For the stated WikiPathways purpose, only get, list, and search are present—no create, update, or delete operations—leaving obvious lifecycle gaps. The extensive non-WikiPathways tools do not contribute to the server's apparent domain coverage and create dead ends for agents expecting pathway management.

Resources