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by co.heista

Dispatch — quantitative-researcher

dispatch_quantitative_researcher

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priorityNostandard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts).
objectiveYesOne sentence stating what "done" looks like — the specific deliverable the specialist must return. 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 (parent may summarize otherwise).
tool_guidanceYesHow the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize.

TDQS

A4.5/5.0
Behavior4/5

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

Discloses that negative findings are deliverable ('Often answers with insufficient-evidence when underlying data is thin'). Annotations already indicate openWorldHint=true and readOnlyHint=false, and description adds context about methodology rigor, which is valuable beyond 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.

Conciseness5/5

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

Four sentences, front-loaded with purpose and usage, no wasted words. Clearly structured with positive use cases, behavioral notes, return format, and explicit exclusions.

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?

Given 5 parameters (4 required) and no output schema, the description covers purpose, usage, behavioral traits, and return format (4-axis summary, table, gaps, sources). It provides all necessary context for an agent to decide when and how to invoke this tool.

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 coverage is 100%, so baseline is 3. Description does not add extra meaning for parameters beyond what schema provides, but that is acceptable given full schema documentation.

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 full methodology context. Provides specific example briefs like 'effect size of X' and explicitly distinguishes from sibling tools (dispatch_desk_researcher, dispatch_qualitative_researcher).

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 that turn on numbers done rigorously') and when not to use ('NOT for: topic landscapes / community language patterns'), naming alternative sibling tools directly.

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