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Heista

by co.heista

Dispatch (async) — trend-researcher

dispatch_trend_researcher_async
Idempotent

Dispatch to the TREND RESEARCHER — recency-dominant trajectory investigation. Use for: "is X a real trend / what is happening with X right now / where is X headed / what is driving X". Distinguishes trend from spike, signal from noise, real shift from echo chamber. Commits to falsifying conditions before searching. Returns: 4-axis Trend assessment (Reality / Magnitude / Direction / Horizon) + Current state + Baseline + trajectory + Drivers + Counter-signals + Sources. NOT for: static landscape questions (use dispatch_desk_researcher) / entity teardowns (use dispatch_market_analyst) / numerical analysis (use dispatch_quantitative_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?

Annotations already indicate non-read-only, open world, and idempotent. Description adds: returns job_id, runs durably on Vercel Workflow (no 300s timeout), commits to falsifying conditions, and details output format. No contradiction.

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?

Description is relatively long but well-organized with clear sections (purpose, examples, output, not-for, async behavior). Every sentence adds value, though slight verbosity prevents a 5.

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, multiple siblings, detailed output), the description fully explains what the tool does, how to use it, when to choose async, and how to retrieve results. No missing information.

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% and each parameter has a description. The tool description does not add significant new meaning beyond the schema, though it contextualizes the overall workflow. Baseline 3 is appropriate.

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's purpose: trend research with recency-dominant trajectory investigation. It provides example queries and distinguishes from sibling tools like dispatch_desk_researcher, dispatch_market_analyst, and dispatch_quantitative_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 lists when to use (e.g., 'is X a real trend') and when not to use (e.g., static landscape questions, entity teardowns). Also gives async-specific guidance: use when >90s, poll with get_dispatch_result, and notes idempotency.

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