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Heista

by co.heista

Dispatch (async) — quantitative-researcher

dispatch_quantitative_researcher_async
Idempotent

Dispatch to the QUANTITATIVE RESEARCHER — numerical analysis with full methodology context. Use for: briefs that turn on numbers done rigorously — "what is the documented effect size of X / what does the data say about Y / quantify the impact of Z". Every load-bearing number carries sample frame, sample size, measurement instrument, time window. Often answers with insufficient-evidence when underlying data is thin (negative findings are deliverable). Returns: 4-axis Quantitative summary (Value / Methodology rigor / Effect size / Robustness) + Numerical findings table + Methodology gaps + Sources. NOT for: topic landscapes (use dispatch_desk_researcher) / community language patterns (use dispatch_qualitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priorityNostandard (default) uses the specialist's production model; deep uses its escalation model.
objectiveYesOne sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5).
boundariesYesIn scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social").
output_formatYesThe shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly.
tool_guidanceYesHow the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize.

TDQS

A4.6/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: async return of job_id, durable execution on Vercel Workflow, idempotency, negative findings as deliverable, and return structure. No contradiction with annotations.

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?

Well-structured and concise, front-loaded with purpose, then usage boundaries, then async specifics. Bullet points for NOT-for and ASYNC version are effective. Every sentence adds value.

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?

Covers async workflow, idempotency, polling mechanism, and return summary. With no output schema, the description provides enough context for an agent to understand what to expect and how to handle the response. Could be slightly more precise on the return format but sufficient.

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% with good individual parameter descriptions. The tool description adds contextual meaning (e.g., reference to delegation contract for objective) but does not substantially enhance parameter-level details beyond what the schema provides.

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?

Clearly states it dispatches to a quantitative researcher for numerical analysis with methodology context. Distinguishes from sibling tools like dispatch_desk_researcher and dispatch_qualitative_researcher by specifying it is for numbers-driven briefs.

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?

Explicitly states when to use (briefs requiring rigorous numerical analysis) and when not to use (topic landscapes for dispatch_desk_researcher, community language patterns for dispatch_qualitative_researcher), providing clear alternatives.

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

A3.7/5.0
Disambiguation4/5

Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.

Naming Consistency3/5

Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.

Tool Count2/5

112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.

Completeness4/5

The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.

Resources