Skip to main content
Glama

propose_changeset

Promote pending timeline entries into a changeset, formalizing project memory updates as versioned proposals.

Instructions

Promote pending timeline entries into a changeset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNo
messageYes
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden of behavioral disclosure. The verb 'promote' implies a state-changing operation, but the description never states what actually happens to the timeline entries (are they consumed, deleted, marked?), whether changes are reversible, whether authorization is required, or what the tool returns. Consequences of the mutation are entirely undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is grammatically efficient and front-loads the core action with no wasted words. However, it is under-specified: for a tool with an undocumented required parameter, this brevity crosses from conciseness into incompleteness. It reads cleanly but does too little documentation work.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description is the only documentation for a state-changing tool. It fails to explain the `message`/`intent` parameters, the side effects of promotion, or what a changeset is. Notably the JSON response wasn't part of this review, but without a schema defining the return, an agent cannot know what to expect after invoking it. Inadequate for a mutation with undocumented parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not compensate at all — it never mentions `message` (required) or `intent` (optional). The agent has no idea what these values should contain (e.g., is `message` a commit note? does `intent` describe the change's purpose?). With zero coverage and zero description help, both parameters are effectively opaque.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action and resource: 'Promote pending timeline entries into a changeset.' The verb+resource is clear, but it does not distinguish itself from its 'propose_' siblings (propose_graph_update, propose_belief). Also relies on undefined domain jargon ('timeline entries', 'changeset') which assumes context. Clear but no sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus propose_graph_update or propose_belief. There is no stated condition, prerequisite, or when-not-to-use guidance. The reader can only infer usage from the name, which is not sufficient routing information among three similarly-named siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mahabubul470/gpp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server