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Glama

Guidetopharmacology

by io.github.pipeworx-io

Target Interactions

target_interactions
Read-onlyIdempotent

List the quantitative ligand interactions for a protein target in the Guide to PHARMACOLOGY (IUPHAR/BPS). Given a GtoPdb target id (from search_targets), returns the ligands acting on it with interaction type (Agonist/Antagonist/Inhibitor/etc.), action, and binding affinity (e.g. pKi/pIC50). Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax interactions to return (default 25).
target_idYesGtoPdb target id, e.g. 1797 for EGFR.

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: +[
+  {
+    "target_id": 1797
+  },
+  {
+    "limit": 50,
+    "target_id": "1797"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds value by specifying the return content (interaction type, action, binding affinity) and noting that the tool is 'Keyless' (no API key required). No contradictions 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?

The description is very concise: three sentences, each adding essential information. The purpose is front-loaded, and there is no redundant or wasted text. 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 rich annotations (readOnlyHint, idempotentHint, openWorldHint) and complete parameter schema, the description adequately covers return values and context (e.g., 'Keyless'). It explains what the tool returns without needing an output schema. The description is complete for a query tool.

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 clear parameter descriptions for target_id (including example) and limit (including default). The description reinforces the meaning of target_id by referencing search_targets, but this adds minimal value beyond the schema. 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?

The description clearly states the verb 'List' and the resource 'quantitative ligand interactions for a protein target' in the IUPHAR/BPS database. It specifies what is returned (ligand interactions with type, action, binding affinity) and how to identify the target, effectively distinguishing it from sibling tools like 'ligand_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 provides a clear prerequisite: 'Given a GtoPdb target id (from search_targets)', guiding the agent to first use search_targets. However, it does not explicitly state when not to use this tool or list alternatives, which would strengthen the guidance.

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.

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