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Santismm Knowledge — Harness Engineering, Agentic AI & Governance

by com.santismm
OverviewDiscussionsAdmin

List the professions with an AI model ranking

list_professions
Read-onlyIdempotent

List the professions that have a ranking of frontier AI models built from independent benchmark results (lawyer, accountant, developer, salesperson, translator, engineer…), each marked as a direct or an indirect evaluation, with the model that ranks first today. Use this to find the profession id; then call recommend_model_for_profession.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoRestrict to one family: knowledge, business, creative, tech, technical or care.
localeNoLanguage of the names. The benchmarks site publishes en and es; other locales fall back to English.
evaluationNoRestrict to direct evaluations (a benchmark reproduces the job) or indirect ones (general tests only).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedOutput schema / properties / results / items / properties / not_measured
      Added value: +{
+  "description": "What no benchmark in the catalogue evaluates at all. Applies even when `unmeasured_tasks` is 0.",
+  "type": "string"
+}
    • changedOutput schema / properties / results / items / properties / unmeasured_tasks / description
      Previous value: -"How many of its listed tasks no benchmark measures yet."New value: +"How many of its listed TASKS no benchmark measures yet. Zero is complete task coverage, not a complete measurement — `not_measured` is a different axis and applies anyway."
    • changedOutput schema / properties / results / items / required
      Previous value: -[
-  "id",
-  "name",
-  "group",
-  "evaluation",
-  "first",
-  "unmeasured_tasks",
-  "canonical_url",
-  "dataset_version",
-  "resource_uri"
-]New value: +[
+  "id",
+  "name",
+  "group",
+  "evaluation",
+  "first",
+  "unmeasured_tasks",
+  "not_measured",
+  "canonical_url",
+  "dataset_version",
+  "resource_uri"
+]
  2. Added

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is fully covered. The description's extra detail (direct/indirect marking, top-ranked model) is return-content information that the output schema already carries, so it adds little behavioral context beyond the structured fields.

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 purpose is front-loaded in the first sentence and the routing instruction follows immediately. The opening sentence is dense with stacked clauses, but every clause carries distinguishing information, so it stays close to earning 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?

With an output schema present, the description need not explain return fields, and annotations cover safety. It supplies purpose, filtering intent and the follow-up tool, so an agent has everything needed to call it and chain correctly.

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%, so group, locale (including the English-fallback caveat) and evaluation are all documented in the schema itself. The description alludes to the direct/indirect distinction but adds no syntax or semantics beyond what the schema already provides, making the baseline 3 correct.

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 and resource (list professions) and pins down the payload precisely: professions with an AI-model ranking from independent benchmarks, marked direct/indirect, with today's top model. The parenthetical examples and the 'direct or indirect evaluation' detail make it unmistakably distinct from sibling list_* tools.

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?

Gives an explicit purpose statement and next step: 'Use this to find the profession id; then call `recommend_model_for_profession`.' This names the downstream tool and the condition that selects it, leaving no ambiguity about when to reach for this versus the recommendation tool.

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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