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Fodda Synthetic Expert Consult

by ai.fodda

check_deliverable_status

Read-onlyIdempotent

Poll a deliverable commissioned with request_deliverable. Pass the job_id from that response. Returns the current status ("working" | "completed" | "failed") and, once completed, the artifact links to present to the user. Polling is free. Deliverables typically take a few minutes — poll every ~15–30s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job_id returned by request_deliverable.
userIdNoOptional user identifier.

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: polling is free, typical duration is a few minutes, recommended polling interval is 15-30s. These details help the agent manage expectations and behavior. No contradiction with annotations (readOnlyHint, idempotentHint, destructiveHint all align).

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 concise sentences: first defines purpose, second explains input and output, third gives practical polling guidance. No fluff, front-loaded, every sentence 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 polling tool, the description covers all necessary aspects: what triggers it (request_deliverable), what to pass (job_id), what it returns (status and artifact links), how often to poll (15-30s), and cost (free). No output schema needed as the description lists return fields. Complete for the use case.

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% (both parameters described). The description adds value by explicitly linking job_id to request_deliverable's response, which clarifies the parameter's origin. For userId, no extra context is given, but the schema description is sufficient. The tool name also implies the parameter semantics.

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?

The description clearly states the verb ('Poll') and resource ('deliverable status'), and explicitly ties it to the sibling tool 'request_deliverable' by referencing job_id. It specifies the output (status and artifact links), leaving no ambiguity about what the tool does.

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 description gives clear context on when to use the tool: after request_deliverable, with job_id. It provides polling frequency guidance ('every ~15–30s') and typical duration ('a few minutes'). Although it doesn't explicitly state when NOT to use, the context is sufficient for correct selection.

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

All Fodda Synthetic Expert Consult toolsInspect
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TDQS

A4.4/5.0
Disambiguation4/5

The tools are mostly distinct: search_graph, get_node, get_neighbors, get_evidence, and get_label_values all relate to graph exploration, but each has a clear role (search vs. profile vs. relationships vs. evidence vs. label enumeration). consult_analyst and consult_human_agent are similar in purpose and wording, but the 'Synthetic' vs. 'Human Agent' distinction in names and descriptions is sufficient to keep them separated.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern, with sensible verbs like get_, list_, search_, request_, check_, consult_, and generate_. The paired request_deliverable / check_deliverable_status and list_analysts / consult_* relationships are clear and predictable.

Tool Count5/5

14 tools is well within the ideal 3–15 range and each tool appears justified: graph discovery, trend exploration, evidence retrieval, expert consultation, deliverable commissioning, visual generation, account/capability introspection, and analyst listing. No redundant extras or obvious bloat.

Completeness5/5

The tool set covers the full workflow promised by the server: discovering graphs and analysts, searching and drilling into trends, retrieving evidence, consulting experts, commissioning and polling deliverables, generating visuals, and checking account status. There are no significant dead ends or clearly missing operations for the stated expert-consultation purpose.

Resources