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Graphlog Career

Read career record schema

get_career_schema
Read-onlyIdempotent

Return the career record's JSON Schema: every section and every field, with what each holds, its format and when it is absent. It serves questions about what the career record can keep and what a field accepts.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the description's marginal burden is low. It does add value by disclosing the shape of the payload (all sections and fields, with formats and absence rules), which matters because there is no output schema. However, it says nothing about the response envelope, size, or cost of returning a full schema, so it stays at a moderate level.

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?

Two sentences, no filler, with the return content front-loaded before the use-case clarification. Every clause adds information the agent did not already have from the tool name.

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

Completeness4/5

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

For a no-parameter, read-only schema-introspection tool with no output schema, the description covers what comes back well enough to decide whether to call it. The only gap is that it never states the size or granularity limit of the returned schema nor confirms there is no filtering, which a caller reasoning about context cost might want.

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

Parameters4/5

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly makes no parameter claims and does not invent options that the empty schema would contradict.

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?

Specific verb+resource: 'Return the career record's JSON Schema', followed by an enumeration of what that schema contains (sections, fields, formats, absence conditions). The clause 'It serves questions about what the career record can keep' implicitly separates it from data-returning siblings like get_career_record or show_career_record, but none of those siblings are named explicitly.

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 second sentence gives a clear use context: questions about what the career record can hold and what a field accepts. This is a usable when-to-use signal without naming the alternative tools or stating exclusions (e.g., 'use get_career_record for actual values'), so it stops short of the 5 bar.

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

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