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Glama

Guidetopharmacology

by io.github.pipeworx-io

Search Ligands

search_ligands
Read-onlyIdempotent

Search the Guide to PHARMACOLOGY (IUPHAR/BPS) — an expert-curated pharmacology database — for drug ligands by name. Returns matching ligands with their GtoPdb ligand id, type (e.g. Synthetic organic, Peptide, Antibody, Metabolite), and approval status. Use the returned ligand id with ligand_interactions to find which protein targets it acts on. Keyless. Complements ChEMBL/DrugBank.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesLigand name or fragment to search for, e.g. "aspirin" or "imatinib".
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": "aspirin"
+  },
+  {
+    "limit": 10,
+    "name": "imatinib"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive nature. Description adds 'keyless' and specifies output fields, but does not significantly extend 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?

Three sentences perfectly front-loaded with essentials: database, verb, output, usage chain, and keyless status. Zero wasted words.

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?

With strong annotations and a simple 2-param schema, description fully covers purpose, usage, output, and linkage to other tools. No gaps remain.

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% and parameter descriptions are clear. Description adds no additional meaning beyond schema, so baseline of 3 is appropriate.

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?

Uses specific verb 'Search' and resource 'ligands' in Guide to PHARMACOLOGY. Distinguishes from siblings like search_targets and ligand_interactions by explicitly stating the database and complementing other databases.

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 instructs to use returned ligand id with ligand_interactions, and notes it complements ChEMBL/DrugBank. Almost perfect, but lacks explicit when-not scenarios.

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