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Glama

Guidetopharmacology

by io.github.pipeworx-io

Search Targets

search_targets
Read-onlyIdempotent

Search the Guide to PHARMACOLOGY (IUPHAR/BPS) — an expert-curated pharmacology database — for protein targets by name. Returns matching targets with their GtoPdb target id, abbreviation, and type (e.g. GPCR, CatalyticReceptor, Enzyme, Transporter). Use the returned target id with target_interactions to find ligands that bind it. Keyless. Complements ChEMBL/DrugBank.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesTarget name or fragment to search for, e.g. "EGFR" or "dopamine receptor".
limitNoMax results to return (default 15).

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: +[
+  {
+    "name": "EGFR"
+  },
+  {
+    "limit": 20,
+    "name": "dopamine receptor"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds value by stating the tool is keyless (no authentication required) and specifying the returned data types. 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?

The description is three sentences long, front-loads the purpose, and provides essential usage guidance without unnecessary words. It is efficient though could be slightly more structured (e.g., bullet points).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lists returned fields (target id, abbreviation, type) and gives examples of types, which is helpful. It also mentions complementing ChEMBL/DrugBank. It lacks details on pagination or error handling, but annotations and schema cover most needs.

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% with both parameters described and examples provided. The description restates the 'name' parameter but adds the important usage hint that the returned target id can be used with target_interactions, which is not in the schema. This earns a baseline score of 3.

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 the tool searches the Guide to PHARMACOLOGY for protein targets by name, specifies the returned fields (target id, abbreviation, type), and gives examples of target types (GPCR, etc.). It also contrasts with sibling tools like search_ligands and target_interactions by suggesting using the returned id with target_interactions.

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?

The description explicitly advises using the returned target id with target_interactions to find ligands, and notes that the tool is keyless and complements ChEMBL/DrugBank. It does not explicitly state when not to use this tool, but the usage guidance is clear and actionable.

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

All Guidetopharmacology toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) that differ only subtly, and a large set of prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) that can be easily confused. Entity tools like entity_profile, recent_changes, and compare_entities also overlap significantly. An agent would struggle to pick the right tool without careful reading.

Naming Consistency2/5

Tool names mix snake_case (ask_pipeworx, deep_research, forget) and descriptive phrases without a consistent verb_noun pattern. Some start with verbs (compare, generate, scan) while others are nouns or compound phrases (pipeworx_trending, polymarket_fill_risk). This inconsistency makes it hard to predict tool names.

Tool Count2/5

With 35 tools, this MCP server is overly large and covers many diverse domains (data querying, prediction markets, pharmacology, npm scanning, brand visibility, etc.). Typically, a well-scoped server has 5-15 tools; 35 is excessive and suggests a lack of focus, making it unwieldy for an agent to manage.

Completeness2/5

Despite the large number of tools, the server has notable gaps. For example, the pharmacology section only offers search and interaction tools but no create/update/delete. The memory tools are limited to save/recall/forget. Many meta-tools (discover_tools, suggest_questions) exist but add little substance. The server covers many domains superficially rather than providing full lifecycle coverage for any one domain.

Resources