Skip to main content
Glama

Heista

by co.heista

Dispatch (async) — qualitative-researcher

dispatch_qualitative_researcher_async
Idempotent

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). 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

A4.4/5.0
Behavior4/5

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

Annotations already indicate idempotentHint=true, and the description reinforces this with 'Idempotent: same brief + same org reuses the same job_id'. It also adds useful behavioral details like the async workflow (returns { job_id } immediately, no 300s timeout, durable Vercel Workflow) and describes the specialist's output contents. Does not contradict annotations.

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

Conciseness4/5

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

The description is moderately long but well-structured into clear sections: purpose, use cases, output, alternatives, async specifics. It front-loads the core purpose and is efficient without excessive fluff.

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 tool's complexity (async, polling, idempotent, many sibling tools), the description covers all necessary aspects: why to use, when to use async, how to poll, output contents, and exclusions. No output schema exists, but the description details the return format sufficiently.

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% with clear descriptions for each parameter. The description does not add significant meaning beyond the schema, but the overall context of what the tool does helps infer parameter usage. No explicit parameter examples or cross-references are provided.

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 tool dispatches to a qualitative researcher for thematic synthesis from unstructured text, with specific example questions. It distinguishes from sibling tools like dispatch_quantitative_researcher and dispatch_social_listening_researcher, making the purpose unmistakable.

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?

The description explicitly states when to use ('Use for:') and when not to use ('NOT for:') with concrete alternatives. It also specifies when to prefer the async version (over 90s expected duration) and provides polling instructions via get_dispatch_result.

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

All Heista toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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