homologyRetrieve 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.
| Name | Required | Description | Default |
|---|---|---|---|
| species | No | ||
| identifiers | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "identifiers": [
+ "9606.ENSP00000269305"
+ ]
+ },
+ {
+ "identifiers": [
+ "9606.ENSP00000269305",
+ "9606.ENSP00000005339"
+ ],
+ "species": 10090
+ }
+]Output 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"
+}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.
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.
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.
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.
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.
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.
Add one secure layer between your agents and this server.
Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying data catalog. The six polymarket_* tools also blur together (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), and resolve vs resolve_entity is an outright collision an agent will likely misselect.
There is a solid verb_noun core (list_subscriptions, scan_dependency, validate_claim, suggest_questions, compare_entities) but it is mixed with bare nouns (enrichment, homology, interactions, network) and product-prefixed names (pipeworx_feedback, polymarket_edges, ask_pipeworx). No single consistent pattern holds across the set, though the clusters are internally predictable.
At 36 tools this is well above the 25+ threshold for 'too many,' and the sprawl is not justified by a single coherent domain—prediction markets, bioinformatics, brand visibility, npm scanning, and subscription management are jammed together. The count makes the tool surface hard to navigate even with good descriptions.
Within each major cluster the lifecycle feels covered: memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), STRING-DB (resolve/homology/interactions/network/enrichment), and Polymarket analysis (scan/edge/arb/fill-risk/track) all form reasonably complete workflows. The main gap is that the server attempts so many domains that none is exhaustively deep, but there are no critical dead ends.