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Heista

by co.heista

Dispatch — qualitative-researcher

dispatch_qualitative_researcher

Dispatch to the QUALITATIVE RESEARCHER — thematic synthesis from unstructured text (interviews, reviews, forum threads, customer language). Use for: "what are the 2-3 recurring themes in how D2C founders talk about X / what language is being used around Y / what are the patterns in customer reviews of Z". Every theme carries evidence count, triangulation status, ≥1 verbatim quote, outlier-check note. SOLVES the Reddit/X/Substack named-operator voice retrieval gap that legacy search tools could not fill. Returns: Corpus + Sampling + Coding methodology + 4-axis Themes table + Theme synthesis + Outlier voices + Saturation assessment + Sources. NOT for: quantitative effect sizes (use dispatch_quantitative_researcher) / multi-platform discourse mapping (use dispatch_social_listening_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?

Description adds value beyond annotations by detailing the output structure (Corpus, Sampling, etc.) and noting that each theme includes evidence count, quotes, and outlier checks. It does not fully explain side effects (e.g., whether results are stored or if there are costs), but annotations already indicate mutability and external data access.

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?

Description is well-organized with clear sections for use cases, NOT for, and returns. Every sentence contributes useful information without redundancy.

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 the absence of an output schema, the description thoroughly explains the return structure and methodological components. It also addresses the tool's niche and limitations relative to siblings, making it highly informative for correct invocation.

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?

Input schema covers all 5 parameters with descriptions (100% coverage). The description does not add additional meaning beyond the schema; baseline 3 is appropriate as it does not detract but does not enhance.

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?

Description clearly states the tool performs thematic synthesis from unstructured text, gives specific use-case examples, and distinguishes from sibling tools like dispatch_quantitative_researcher and dispatch_social_listening_researcher by listing what it is NOT for.

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 ('what are the 2-3 recurring themes...') and when NOT to use, referencing alternative tools for quantitative effect sizes and multi-platform discourse mapping. This provides clear decision support for the agent.

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