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String Db

by io.github.pipeworx-io
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Homology

homology
Read-onlyIdempotent

Retrieve STRING-DB homology mappings for a list of protein identifiers (gene symbols or accessions) within a given NCBI taxonomy species (default 9606=human), returning cross-species homolog relationships.

Input Schema

NameRequiredDescriptionDefault
speciesNo
identifiersYes

Output Schema

NameRequiredDescriptionDefault
countYesNumber of items returned.
itemsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "identifiers": [
+      "9606.ENSP00000269305"
+    ]
+  },
+  {
+    "identifiers": [
+      "9606.ENSP00000269305",
+      "9606.ENSP00000005339"
+    ],
+    "species": 10090
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "Number of items returned.",
+      "type": "integer"
+    },
+    "items": {
+      "items": {
+        "properties": {
+          "homologs": {
+            "description": "List of homologous proteins",
+            "items": {
+              "properties": {
+                "ncbiTaxonId": {
+                  "description": "Homolog NCBI taxonomy ID",
+                  "type": "number"
+                },
+                "preferredName": {
+                  "description": "Homolog protein name",
+                  "type": "string"
+                },
+                "stringId": {
+                  "description": "Homolog STRING ID",
+                  "type": "string"
+                },
+                "taxonName": {
+                  "description": "Homolog species name",
+                  "type": "string"
+                }
+              },
+              "type": "object"
+            },
+            "type": "array"
+          },
+          "ncbiTaxonId": {
+            "description": "Query NCBI taxonomy ID",
+            "type": "number"
+          },
+          "preferredName": {
+            "description": "Query protein name",
+            "type": "string"
+          },
+          "stringId": {
+            "description": "Query protein STRING ID",
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "items",
+    "count"
+  ],
+  "type": "object"
+}
  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 read-only, idempotent, open world, and non-destructive. The description adds useful context: data source (STRING-DB), accepted identifier formats (gene symbols or accessions), and default species (9606=human). This goes beyond the annotations without contradicting them.

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 entire description is a single sentence that packs all essential details: source, input types, default parameter, and output. It is front-loaded with the verb and has zero fluff.

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 annotations covering safety (read-only, idempotent) and an output schema present, the description sufficiently covers the tool's purpose and input semantics. No critical information is missing for a simple query tool.

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 description coverage is 0%, so the description compensates by explaining 'identifiers' as protein identifiers (gene symbols or accessions) and 'species' as an NCBI taxonomy species with a default of 9606. This adds semantic meaning beyond the bare property names and schema types.

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 uses a specific verb 'Retrieve' and names the resource 'STRING-DB homology mappings', along with input types (protein identifiers, species) and output nature (cross-species homolog relationships). This clearly distinguishes it from sibling tools like 'network' or 'enrichment'.

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?

It provides clear context: you use this tool when you have protein identifiers and a species and want cross-species homologs. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.

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

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