One, seven and thirty-day activity windows separating originals from replications, distinct proposals measured, seconding thresholds reached and explicit ratifications. Raw measurement-submission volume is never called proposal progress.
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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, the description carries the full disclosure burden. It does add genuine semantic context — metric components (distinct proposals, seconding thresholds, ratifications), exclusion of replications, and the 1/7/30-day windows — and the final sentence serves as an interpretive guardrail. But it never states that the operation is read-only or what the returned payload looks like, leaving those traits to be inferred from the 'get_' prefix.
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?
At two sentences it is appropriately brief and every phrase carries meaning, but the first sentence is a grammatically fragmented enumeration that is hard to parse on first read. The central claim is not front-loaded as a clear verb-plus-object statement.
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, the description covers the metric's semantics well, which is the main thing an agent needs to interpret results. Yet with no output schema, the return format is never described — the agent cannot tell whether the result is a single throughput figure, a per-window breakdown, or counts versus rates.
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 input schema is empty, so there are no parameters to document; the zero-parameter baseline of 4 applies. The description correctly adds measurement context rather than inventing parameter details.
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 defines what the tool measures — activity over 1/7/30-day windows, distinct proposals, seconding thresholds, and ratifications — and adds a useful negative ('raw measurement-submission volume is never called proposal progress'). However, there is no explicit verb or statement of what the tool returns; the first sentence is a string of noun fragments ('...activity windows separating... distinct proposals measured...') that forces the agent to infer the operation from the 'get_' prefix.
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 guidance is given for when to choose this tool over any of its 40+ siblings such as get_progression, get_adoption_trends, or list_measurements. The only contrast offered is a semantic disclaimer about what does not count as progress, which clarifies interpretation but does not route the agent to an alternative.
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.
