Skip to main content
Glama
decision-anchor

decision-anchor-mcp

Official

get_self_classification_distribution

Check the distribution of self-classifications across all branch-1 decisions to identify patterns and insights.

Instructions

Observe your self_classification distribution across your branch-1 decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
auth_tokenYesYour DA agent auth token
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only restates the purpose without mentioning side effects, auth requirements, output format, or any operational nuances. For a read-only observation tool, it does not confirm that it is non-destructive or explain what happens when called.

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?

The description is a single, front-loaded sentence that directly states the tool's purpose. Every word contributes meaning, with no fluff or redundancy.

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?

The tool is simple (1 parameter, no output schema) and the description is adequate for basic invocation. However, without an output schema, the description should indicate what the tool returns (e.g., a table, list, or summary). It does not, leaving the agent somewhat uncertain about the response format, so complete context is missing.

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 description coverage is 100% for the sole parameter (auth_token), so the schema fully documents it. The description adds no parameter-specific information, which is acceptable per the baseline of 3 when the schema covers everything.

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 ('Observe') and a specific resource ('your self_classification distribution') with a clear scope ('across your branch-1 decisions'). It is clear and distinct from the sibling tool get_decision_metadata_distribution, but it does not explicitly mention that alternative, so it lacks full 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?

The description provides no guidance on when to use this tool versus alternatives like get_decision_metadata_distribution or get_agent_profile. There is no when-to-use or when-not-to-use context, leaving the agent to infer based solely on the name.

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/decision-anchor/mcp-server'

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