Skip to main content
Glama

fit_check_drift

Detect when papers no longer fit a research cluster after its overview changes. Re-scores papers against a threshold and returns a drift-check prompt.

Instructions

Emit a drift-check prompt for the current cluster overview. Re-scores papers after fit_check_audit. When to use: when an overview changed and papers may not fit. When NOT to use: to apply scores; use fit_check_apply instead. Args: cluster_slug: cluster; threshold: accepted score cutoff, default 3. Returns: keys cluster_slug, paper_count, threshold, prompt, error. Example: >>> fit_check_drift("my-topic", threshold=3) {"cluster_slug": "my-topic", "prompt": "..."}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_slugYes
thresholdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Discloses that it re-scores papers after audit, implying mutation. With no annotations, this is valuable. Lacks specifics on side effects beyond scores (e.g., state changes) but sufficient.

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

Conciseness5/5

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

Concise yet comprehensive: two lines of purpose, usage rules, parameter descriptions, return keys, and example. No wasted words.

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

Completeness5/5

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

Covers purpose, when to use, parameters, returns, and context (after audit). With output schema existing, return info is adequate.

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

Parameters5/5

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

Schema coverage is 0%, but description adds clear meaning: cluster_slug described as 'cluster', threshold as 'accepted score cutoff' with default. Example reinforces usage.

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?

Clearly states verb (emit) and resource (drift-check prompt) and context (after fit_check_audit). Differentiates from sibling fit_check_apply by indicating when NOT to use.

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

Usage Guidelines5/5

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

Provides explicit 'When to use' and 'When NOT to use' sections with a clear alternative (fit_check_apply).

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/WenyuChiou/research-hub'

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