One proposal in full: measurements and their human evidence story, evidence readiness, conditional progression path, votes, language adoption, supersession links, deterministic robustness, and report-only disclosed-linked-seconder coverage. Metric semantics keep token cost and comprehension distinct.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The proposal slug. |
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?
No annotations are present, so the description carries the full burden. It hints at one boundary ('report-only disclosed-linked-seconder coverage') but never states that this is a read-only operation, what side effects could occur, or how the response behaves. The 'Metric semantics' sentence is too cryptic to count as meaningful behavioral disclosure.
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 text is compact and front-loaded with 'One proposal in full', but the long comma-separated list and the cryptic final sentence make it dense and hard to parse. It is concise in length but not optimally structured for comprehension.
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 single-parameter getter with no output schema, the description enumerates the main return content, which helps. It is still incomplete for an unfamiliar agent because the domain concepts are unexplained and no usage context is given; however, the basic 'get one thing by slug' contract is present.
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 covers the only parameter (slug: 'The proposal slug.') at 100%, so the description does not need to add parameter details. It adds no extra semantics but also does not need to.
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?
Opens with 'One proposal in full', clearly scoping the operation to a single proposal rather than a list, and enumerates the returned facets (measurements, votes, readiness, supersession, etc.). However, the jargon ('human evidence story', 'disclosed-linked-seconder coverage') is opaque and prevents a 5.
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 explicit guidance on when to call this versus siblings like get_progression or get_measurement; the only hint is 'One proposal in full'. There are no prerequisites, exclusions, or alternative-routing statements.
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.
Most tools have unique resource nouns, but several clusters overlap in purpose: get_progression, get_decisions, get_queue, and get_dispute_triage all describe next-action/status views, and the flagship/runbook/register families require careful reading to distinguish. The detailed descriptions help an agent choose correctly, but boundaries are not always obvious.
There is a strong get_/list_ convention for reads and imperative verbs for writes, making the set largely predictable. Minor deviations such as whoami, how_to_participate, my_suggestions, propose, and second break the pattern slightly but do not make names chaotic.
47 tools is far beyond the well-scoped band and will overwhelm an agent's tool-selection surface. Even if the domain is complex, many read-only projections could be consolidated into fewer parameterized tools.
The core lifecycle is well covered: propose, second, measure, vote, withdraw/replace, retract, and attempt management are all present, with extensive read support. Minor gaps exist around explicit recertify/dispute-settlement write tools and there is no direct edit operation, but the existing supersession/correction mechanisms largely cover those needs.
