resolveMap free-text identifiers (gene symbols, accessions) → STRING identifiers.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Best-N matches per input (default 1). | |
| species | No | NCBI taxonomy id (default 9606). | |
| 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": [
+ "TP53",
+ "BRCA1",
+ "MYC"
+ ]
+ },
+ {
+ "identifiers": [
+ "ENSP00000269305"
+ ],
+ "limit": 5,
+ "species": 9606
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "items": {
+ "properties": {
+ "annotation": {
+ "description": "Annotation or description",
+ "type": "string"
+ },
+ "ncbiTaxonId": {
+ "description": "NCBI taxonomy ID",
+ "type": "number"
+ },
+ "preferredName": {
+ "description": "Preferred protein name",
+ "type": "string"
+ },
+ "queryIndex": {
+ "description": "Index of the input identifier",
+ "type": "number"
+ },
+ "queryItem": {
+ "description": "Original input identifier",
+ "type": "string"
+ },
+ "score": {
+ "description": "Match confidence score",
+ "type": "number"
+ },
+ "stringId": {
+ "description": "STRING protein identifier",
+ "type": "string"
+ },
+ "taxonName": {
+ "description": "Species name",
+ "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 the tool as read-only, open-world, idempotent, and non-destructive. The description adds no additional behavioral context (e.g., matching behavior, ambiguity handling, or output specifics) beyond what annotations and schema already provide. No contradictions found.
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 description is a single, efficient sentence that immediately conveys the core function. It is exceptionally concise and well-structured, with no unnecessary words.
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?
Given the tool's relative simplicity and the presence of an output schema and rich annotations, the description is minimally adequate. However, it lacks guidance on use cases or alternatives, and does not explain potential edge cases like multiple matches or failure modes, which would make it more complete.
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?
The input schema covers most parameters with descriptions (limit and species), so the baseline is 3. The description adds meaning to the 'identifiers' parameter by calling them 'free-text identifiers,' which is helpful since that parameter lacks a schema description. However, it does not elaborate on limit or species.
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 clearly states the tool's function with a specific verb ('Map') and resource ('free-text identifiers') and specifies the output ('STRING identifiers'). It is clear and concise, but it does not explicitly distinguish itself from the sibling tool 'resolve_entity', which may serve a similar purpose.
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?
The description provides no guidance on when to use this tool versus alternatives like 'resolve_entity' or other identifier-related tools. It lacks any explicit context for selection or exclusion criteria.
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.