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Glama

Mydisease

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Search MyDisease.info for diseases by free-text name or fielded query. Returns matching hits, each keyed by a MONDO disease id (e.g. "MONDO:0015967") with the best-matching ontology and annotation keys. Use this to resolve a disease name to canonical ontology ids before calling the "disease" tool. Free text like "diabetes" or "asthma" works; fielded queries like "mondo.label:asthma" or "disgenet.xrefs.disease_name:..." narrow the search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoNumber of hits to return, 1-1000 (default 10).
queryYesFree-text disease name (e.g. "diabetes") or a fielded query (e.g. "mondo.label:asthma").
fieldsNoComma-separated list of annotation fields to return (default: all fields).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "diabetes"
+  },
+  {
+    "query": "mondo.label:asthma",
+    "size": 20
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readonly, open-world, idempotent, and non-destructive hints. The description adds context such as hits keyed by MONDO IDs and best-matching keys, and clarifies behavior for free-text vs fielded queries, which is valuable 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.

Conciseness5/5

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

Two concise sentences: first defines purpose, second gives usage guidance and examples. No redundant or irrelevant information. Every sentence earns its place.

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 simple schema and annotations, the description fully covers the use case, parameter behavior, and relationship to siblings. It explains what the tool returns (hits keyed by MONDO ID) despite no output schema, which is sufficient for an AI agent.

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%, so baseline is 3. Description provides examples ('diabetes', 'mondo.label:asthma') and clarifies fielded query syntax, adding meaning beyond the schema's property 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?

Description clearly states the tool searches MyDisease.info for diseases by free-text or fielded query, returning matches keyed by MONDO IDs. It distinguishes itself from the sibling 'disease' tool, saying to use this tool to resolve disease names before calling 'disease'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises to use this tool to resolve disease names to canonical ontology IDs before calling the 'disease' tool. It provides examples for free-text and fielded queries, though it does not explicitly state when not to use it.

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

All Mydisease toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Multiple research/query entry points overlap heavily: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research sit on the same routing core, and validate_claim/bet_research/entity_profile all wrap lookup-and-analyze behavior. The detailed descriptions help within specialized clusters, but the central ask_pipeworx family alone creates real selection ambiguity.

Naming Consistency3/5

All names are lower_snake_case and several families are consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the overall set mixes bare verbs, nouns, and verb_noun composites with no global pattern (disease, metadata, query, entity_profile, generate_llms_txt, validate_claim). It is readable but not predictable across the full 34-tool surface.

Tool Count2/5

34 tools is over the 25+ threshold and the set bundles several distinct domains—disease ontology, Pipeworx data access, prediction markets, AI visibility, npm scanning, memory, and subscriptions—into one server. Each subfamily may be justified, but the combined surface is heavy and makes tool selection harder than the underlying tasks require.

Completeness4/5

The disease domain has query/disease/metadata for search-and-fetch read coverage, and the broader research side has lookup, grounded verification, deep research, entity profiles, comparisons, subscriptions, and memory lifecycle tools. Minor gaps exist (no direct tool to fetch pipeworx:// citation URIs, no disease browsing/pagination), but these are workable rather than blocking.

Resources