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Guidetopharmacology

by io.github.pipeworx-io

Ligand Interactions

ligand_interactions
Read-onlyIdempotent

What proteins a drug acts on — the curated target interactions for a ligand in the Guide to PHARMACOLOGY (IUPHAR/BPS), each with the target PROTEIN NAME, interaction type (Agonist/Antagonist/Inhibitor), action and binding affinity (pKi/pIC50). Pass the drug by name ("imatinib") and it is resolved for you; a GtoPdb ligand id also works. The response always names the ligand it actually read, so an id that turns out to be a different compound is visible rather than silent. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax interactions to return (default 25).
ligandNoDrug/ligand name, e.g. "imatinib". Resolved against the GtoPdb ligand list.
ligand_idNoGtoPdb ligand id, e.g. 4139 for aspirin. Optional — give this OR ligand.

Schema Changelog

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

  1. Changed4 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "ligand_id": 4139
-  },
-  {
-    "ligand_id": "4139",
-    "limit": 40
-  }
-]New value: +[
+  {
+    "ligand": "imatinib"
+  },
+  {
+    "ligand_id": 4139
+  },
+  {
+    "ligand_id": "4139",
+    "limit": 40
+  }
+]
    • addedInput schema / properties / ligand
      Added value: +{
+  "description": "Drug/ligand name, e.g. \"imatinib\". Resolved against the GtoPdb ligand list.",
+  "type": "string"
+}
    • changedInput schema / properties / ligand_id / description
      Previous value: -"GtoPdb ligand id, e.g. 4139 for aspirin."New value: +"GtoPdb ligand id, e.g. 4139 for aspirin. Optional — give this OR ligand."
    • changedInput schema / required
      Previous value: -[
-  "ligand_id"
-]New value: +[]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "ligand_id": 4139
+  },
+  {
+    "ligand_id": "4139",
+    "limit": 40
+  }
+]
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable context: ligand name resolution, the response naming the actual ligand read, and the 'keyless' requirement. These are meaningful behavioral disclosures beyond the 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 concise, well-structured, and front-loads the key purpose. Every sentence earns its place: purpose, data source, return fields, input options, resolution behavior, and keyless note. No fluff.

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?

With no output schema, the description compensates by detailing exactly what is returned. It also explains input resolution and the keyless aspect. Minor gaps like default limit behavior are already covered by the schema, so this is effectively complete for the tool's complexity.

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?

The schema already describes all three parameters with 100% coverage. The description adds extra meaning by clarifying that 'ligand' can be a name that is resolved while 'ligand_id' is the GtoPdb identifier, and that either can be used. This enhances the schema without redundancy.

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 begins with 'What proteins a drug acts on', a specific and informative statement of the tool's purpose. It details the curated target interactions and lists the returned fields (protein name, interaction type, action, affinity), fully distinguishing it from sibling search tools.

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?

Clear context is provided: this tool is for querying target interactions for a ligand, with examples of how to pass a drug name or GtoPdb ID. It does not explicitly mention alternative tools or exclusion criteria, but the purpose is specific enough to guide appropriate use.

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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