submit_job_observations

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

With no annotations present, the description carries the full burden of behavioral disclosure. It states that observations go into a dedup + ranking store and that duplicates are merged, and it says what the tool returns ('stored count + duplicates merged'). It does not cover failure modes or idempotency details, but the main side effects are made transparent.

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

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

The description is three tightly scoped sentences: it front-loads the action and source context, then gives the input shape, then states the return value. Every sentence earns its place with no irrelevant detail.

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

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

For a tool with one required parameter and no output schema, the description provides enough to call it correctly: the observation shape, source context, and return contract are all present. Additional semantics for the optional linkage fields and any prerequisites could make it fully complete, but nothing essential is missing.

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

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

Schema coverage is 0%, so the description must compensate. It does so for the required observations parameter by enumerating the expected object shape ({source, url, title, company, location_text, salary_raw, description, posted_at?}), which goes far beyond the generic array-of-objects schema. The optional task_id, search_id, and observation_type fields are left to inference, which prevents a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action ('Feed jobs') and a specific destination ('the same dedup + ranking store'), and it further scopes the input by source ('the harness's own browser' listing Indeed, CV-Library, LinkedIn, Glassdoor). This makes the purpose clear and helps distinguish it from search-oriented siblings like search_jobs.

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

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

The phrase 'jobs found via the harness's own browser' gives a clear context for when this tool is appropriate, and the listed sources provide concrete criteria. It does not explicitly say when not to use it or name an alternative tool, so it stops short of the top score.

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