networkNetwork image url + tabular interaction data.
| Name | Required | Description | Default |
|---|---|---|---|
| species | No | ||
| identifiers | Yes | ||
| network_type | No | ||
| required_score | No |
| Name | Required | Description | Default |
|---|---|---|---|
| imageUrl | No | URL to network visualization image | |
| interactions | No | Interaction data for network |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "identifiers": [
+ "9606.ENSP00000269305",
+ "9606.ENSP00000138641"
+ ]
+ },
+ {
+ "identifiers": [
+ "9606.ENSP00000269305"
+ ],
+ "required_score": 600
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "imageUrl": {
+ "description": "URL to network visualization image",
+ "type": "string"
+ },
+ "interactions": {
+ "description": "Interaction data for network",
+ "items": {
+ "properties": {
+ "ncbiTaxonId": {
+ "description": "NCBI taxonomy ID",
+ "type": "number"
+ },
+ "preferredName_A": {
+ "description": "First protein name",
+ "type": "string"
+ },
+ "preferredName_B": {
+ "description": "Second protein name",
+ "type": "string"
+ },
+ "score": {
+ "description": "Interaction confidence score",
+ "type": "number"
+ },
+ "stringId_A": {
+ "description": "First protein STRING ID",
+ "type": "string"
+ },
+ "stringId_B": {
+ "description": "Second protein STRING ID",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that the output includes an image URL and tabular interaction data, which is useful, but lacks details about pagination, data structure, or any limitations.
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 six-word fragment, which is under-specified rather than concise. It lacks structure and does not form a complete sentence.
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?
Even with annotations and an output schema, the description fails to convey the tool's core capabilities, making it impossible for an agent to know when to use it and what to expect.
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%; the description does not explain the meaning of 'identifiers', 'species', 'network_type', or 'required_score'. The examples show ENSP IDs and a score threshold, but the description provides no parameter semantics.
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 'Network image url + tabular interaction data' is a fragment that fails to state a clear verb or resource. It doesn't indicate what operation is performed on which entities; the schema's ENSP examples hint at protein interactions, but the description alone is ambiguous.
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?
No usage guidance is provided; the description does not mention when to use this tool versus alternatives like 'interactions' or 'enrichment'.
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.