The live research journal: every internal research document (experiment pre-registrations, incident write-ups, verdicts including nulls), auto-published verbatim by the daily export. Returns the manifest; fetch a document with research_note.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that documents are auto-published verbatim by a daily export, and mentions that verdicts include nulls, which is useful context. However, it stops short of describing the format or contents of the manifest itself.
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 concise and front-loaded. It explains what the tool returns, provides context about the data source, and points to an alternative in just two sentences. Every clause adds value.
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?
For a zero-parameter tool with no output schema, the description adequately conveys the tool's purpose and return value. It names the manifest and points to research_note for individual documents. It could be slightly more explicit about what the manifest contains, but the listed document types give a good sense.
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 tool takes zero parameters, and the schema is empty. Per the baseline for zero-parameter tools, this scores a 4. The description does not need to explain parameters, and it correctly focuses on the return value.
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 purpose: it returns the manifest of all internal research documents. It lists specific types of documents (experiment pre-registrations, incident write-ups, verdicts) and distinguishes itself from the sibling tool research_note, which fetches individual documents.
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 directly instructs the user to use research_note to fetch a document, implying this tool is for obtaining the manifest/list. This explicit alternative differentiation provides clear usage guidance.
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 has a clear, distinct purpose: fleet_overview summarizes all sleeves, sleeve_record drills into one, gate_status tracks the go-live gate, kill_list logs rejected ideas, research_journal lists documents, research_note fetches a single document, and site_guide explains the platform. The only close pair is fleet_overview vs. sleeve_record, but their scopes (global vs. single sleeve) prevent confusion.
All tool names follow the same lowercase snake_case pattern with two tokens separated by an underscore (e.g., fleet_overview, gate_status, research_note). There are no mixed conventions like camelCase or inconsistent verb styles.
Seven tools is ideal for a read-only transparency platform. Each tool covers a distinct aspect of the domain without redundancy, and the count is well within the typical well-scoped 3–15 range.
The tool set fully covers its stated purpose: aggregate and per-sleeve performance, research journal and note retrieval, failure records, gate status, and self-description. There are no obvious dead ends—users can start at the overview, drill into details, and access all published documentation.
