getSingle document by HAL id.
| Name | Required | Description | Default |
|---|---|---|---|
| hal_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| response | No | Search results | |
| responseHeader | No | Response metadata |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "hal_id": "hal-01234567"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Single document lookup response from HAL API",
+ "properties": {
+ "response": {
+ "description": "Search results",
+ "properties": {
+ "docs": {
+ "description": "Array of matching documents (typically 0 or 1)",
+ "items": {
+ "description": "Document object",
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "numFound": {
+ "description": "Total number of matching documents",
+ "type": "integer"
+ },
+ "start": {
+ "description": "Starting offset of results",
+ "type": "integer"
+ }
+ },
+ "type": "object"
+ },
+ "responseHeader": {
+ "description": "Response metadata",
+ "properties": {
+ "QTime": {
+ "description": "Query execution time in ms",
+ "type": "integer"
+ },
+ "params": {
+ "description": "Query parameters echoed back",
+ "type": "object"
+ },
+ "status": {
+ "description": "HTTP status code",
+ "type": "integer"
+ }
+ },
+ "type": "object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive hints. The description adds that it returns a single document rather than a list, which is minor context. No contradiction with annotations.
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 sentence with no unnecessary words. It is front-loaded and perfectly concise for the tool's simplicity.
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 simplicity, one parameter, strong annotations, and an output schema, the description is complete enough. It could mention error behavior, but that's often covered by schema; thus it does not feel lacking.
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 description explicitly mentions 'by HAL id,' which directly explains the hal_id parameter's meaning. Even though schema description coverage is 0%, the description compensates by identifying the parameter's purpose clearly.
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 retrieves a single document by HAL id, which is a specific verb+resource and differentiates from search-like tools. However, it doesn't explicitly name alternative tools or contrast them.
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 implies usage when you have a HAL id and want one document, but it doesn't provide explicit guidance on when not to use it or alternatives to consider. It is implied rather than stated.
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.
Several tools occupy nearly the same role: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are deliberately near-duplicates, while deep_research, validate_claim, bet_research, and the polymarket_* family all route factual questions to overlapping data pipelines. Generic single-word tools like get, search, structure, and author add further ambiguity, making it hard for an agent to confidently pick the right tool.
Names are consistently lowercase snake_case, but they mix verb_noun tools (list_subscriptions, resolve_entity, validate_claim) with bare nouns (author, get, search, structure) and domain-prefixed families (ask_pipeworx, polymarket_*, pipeworx_*). The conventions are readable but not predictable enough to infer behavior from the name alone.
35 tools is heavy for a single server, especially when many are meta-routers or near-variants of each other. The broad data-research scope justifies some breadth, but the surface feels padded with overlapping research and prediction-market tools rather than a tight, well-scoped set.
For its apparent purpose — authoritative data lookup, verification, research, entity profiling, prediction-market analysis, and monitoring — the surface is largely complete: retrieval, grounded answers, deep research, comparison, change tracking, subscriptions, and memory are all covered. Minor gaps exist, such as no direct tool for managing alert delivery or for some of the vague HAL-style operations, but agents can work around these via the router tools.