Skip to main content
Glama

chimera_csm

Optimizes user input, estimates token cost, and proposes a budget for cost-effective AI responses.

Instructions

CALL FIRST on every message. Optimizes input, estimates token cost, proposes budget. Show proposal_text to user for approval. After approval: constrain response to max_output_tokens and use optimized_prompt as effective input.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe user's raw input text to optimize and cost-estimate.
messagesNoOptional conversation history [{role, content}] for full context token count.
modelNoModel for pricing. Default: claude-sonnet-4-6claude-sonnet-4-6
task_complexityNoControls output token estimate. auto=detect from prompt keywords. simple=brief factual answer, moderate=explanation/how-to, complex=code/build/design. Default: autoauto
focusNoOptional task focus/query. Defaults to prompt when omitted.
algorithmNoOptimization algorithm. quantum = query-aware compression. classic = legacy rewrite-only compression.quantum
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the optimization, cost estimation, budget proposal, and the approval workflow. However, it does not disclose potential side effects, storage, or rate limits, which are typical for such a tool.

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 sentences covering the core workflow. It is front-loaded with the key directive 'CALL FIRST on every message.' Could be more structured, but it is efficient.

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?

No output schema is provided, and the description implies return fields (proposal_text, optimized_prompt) but does not describe the full return structure or error handling. For a complex tool with multiple outputs, this is a gap.

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?

Schema coverage is 100%, so each parameter is already documented in the input schema. The description focuses on workflow rather than adding parameter-level meaning. Baseline 3 is appropriate as the schema does the heavy lifting.

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's purpose: it optimizes input, estimates token cost, and proposes a budget. It emphasizes 'CALL FIRST on every message,' distinguishing it from other chimera tools that handle later stages.

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

Usage Guidelines4/5

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

The description provides explicit guidance: call this tool first on every message, and after approval, constrain the response and use the optimized prompt. It does not explicitly list when not to use it, but the workflow is clear.

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/fernandogarzaaa/chimeralang-mcp'

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