Skip to main content
Glama

d3p_compress-context

Reduce chat message array length by keeping only first and last messages, reporting compression percentage.

Instructions

Compress an array of chat messages by keeping first and last, reporting reduction percentage. Category: text | Cost: 10 sats | Endpoint: compress-context Parameters (pass as JSON string): messages (array): Array of chat messages to compress

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description discloses core behavior: it compresses by keeping first and last messages and reports reduction percentage. It also notes cost and endpoint. However, it does not cover edge cases (e.g., empty or single-message arrays), side effects (mutability), or return format details (partially covered by output schema). With no annotations, more detail is warranted.

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

Conciseness4/5

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

The description is concise, with two clear sentences plus a metadata line. The key action is front-loaded. No redundant information; each part earns its place. A minor improvement would be structuring metadata as a list, but it's adequately scannable.

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

Completeness3/5

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

Given the tool has one string parameter with a JSON payload and an output schema exists, the description covers the main intent and parameter structure modestly. However, it lacks explanation of required vs optional fields, message format, and does not provide an example. The presence of an output schema partially mitigates this, but the description could be more complete.

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

Parameters3/5

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

The schema has 0% coverage (only a generic 'params' string), but the description adds meaning by specifying 'messages' as an expected array field. This improves usability but does not fully compensate: it lacks details on message format, optional fields, or other potential parameters within the JSON string.

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

Purpose5/5

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

The description clearly states the tool compresses an array of chat messages by keeping the first and last, and reports reduction percentage. It specifies the resource (chat messages) and the action (compress), making the purpose unmistakable. Although sibling differentiation is not explicit, the unique function is obvious.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention prerequisites, constraints, or exclusions. The description lacks explicit when-to-use and when-not-to-use instructions, making it difficult for an agent to decide contextually.

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/awkie1/d3p-mcp-server'

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