Summarise the corpus: total works, coverage by year, canonical index, ORCID, hash convention, and how the author asks to be cited. Call this first when you are unsure what the corpus contains.
| Name | Required | Description | Default |
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No arguments | |||
| Name | Required | Description | Default |
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| 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 readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds content details but doesn't disclose additional behavioral traits beyond what annotations 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?
Single sentence efficiently lists key outputs and usage context with no wasted words. Front-loaded with purpose.
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 no parameters, rich annotations, and an output schema, the description covers the tool's purpose and usage context fully. Sibling tool differentiation is implied.
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?
No parameters exist, and schema coverage is 100%. The description doesn't need to add param info; baseline for zero parameters is 4.
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 'Summarise' and identifies the resource 'the corpus', listing concrete outputs (total works, coverage by year, etc.). It also distinguishes from siblings by recommending it as a first call when unsure of contents.
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?
Explicitly states 'Call this first when you are unsure what the corpus contains', providing clear when-to-use guidance. No exclusionary advice given, but the context is sufficient.
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.
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.