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Explain field

explain
Read-onlyIdempotent

Explain why a field is what it is: returns the recent derivation/constraint trace records for a path from the in-memory ring buffer (inputs, expression, result). For a constraint use the synthetic path "$constraint:". Trace records can be bulky — pass 'limit' to return only the most recent N.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe model id.
pathYesA canonical JSON Path address, e.g. "$.order.total".
limitNoOptional cap; return only the most recent N trace records.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior; the description adds non-obvious context: data comes from an in-memory ring buffer (recent/ephemeral), records include inputs/expression/result, and traces can be bulky. This clearly goes beyond what annotations provide, with no contradiction.

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?

Three sentences with no waste: front-loaded purpose and return type, then a special-case path, then a practical advisory about bulk output. Every clause earns its place.

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?

For a read-only diagnostic tool with readOnly annotations, the description covers purpose, path syntax, constraint paths, data source, and limit handling. Although there is no output schema, the 'inputs, expression, result' hint gives the agent a sufficient model of the returned trace records.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: 'path' accepts a synthetic '$constraint:<id>' form, and 'limit' is motivated by trace bulk. It doesn't add to 'id', but the schema already documents that clearly.

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?

States a specific verb ('Explain why a field is what it is') and the exact return artifact: recent derivation/constraint trace records for a path. This distinguishes it from siblings such as get_field (current value) or get_history (change history), and the synthetic path special case reinforces its exact scope.

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?

Gives explicit usage direction for constraint paths ('$constraint:<id>') and advises passing 'limit' when trace records are bulky. However, it names no alternatives or exclusions, so an agent must infer from sibling names when to prefer explain over get_field, get_history, or get_audit.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.

Naming Consistency4/5

Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.

Tool Count3/5

27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.

Completeness4/5

The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.