Return public positions carrying an explicit topic tag. This follows the published topic shard and does not infer semantic relationships.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| topic | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds value by specifying 'public positions' and the matching behavior: it follows the published topic shard and does not infer semantic relationships. This gives concrete context about data scope and algorithm that annotations do not provide.
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 two sentences, with the primary action in the first sentence and a clarifying behavioral note in the second. Every word earns its place; no filler or repetition.
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?
The tool is simple (2 params, output schema present), and the description covers the core purpose and behavioral constraint. It lacks an explicit alternative-tool reference, but the annotations and output schema provide sufficient contextual coverage for the agent to invoke it correctly.
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 must compensate. It explains that 'topic' refers to an explicit tag, directly clarifying the required parameter's meaning. The optional 'limit' is not explained, but its name and default in the schema make it self-evident.
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 starts with a clear verb and resource: 'Return public positions carrying an explicit topic tag.' It further distinguishes itself from semantic search tools by stating it 'does not infer semantic relationships,' which differentiates it from siblings like search_positions or claims_on_topic.
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 when to use this tool: when you need positions with explicit, non-inferred topic tags. It explicitly clarifies that it follows the published topic shard and avoids semantic inference, giving the agent a basis to choose this over search tools, though it does not name alternative tools directly.
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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Each tool targets a distinct resource or action: overviews, specific claims/papers, searches by different granularities, citation help, and document verification. No two tools overlap in purpose.
All tool names use lowercase snake_case with clear verb-noun or noun-preposition patterns (e.g., get_claim, search_papers, claim_layer_overview). The naming is uniform and predictable.
With 11 tools, the set covers the core operations for exploring a scholarly corpus without being excessive. Each tool serves a clear and necessary purpose.
The tools provide comprehensive coverage for discovering, retrieving, and verifying content from the corpus: overviews, searches at paper/claim/post level, direct retrieval, and document verification. No obvious missing operations for the intended use case.