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Heista

by co.heista

Dispatch — trend-researcher

dispatch_trend_researcher

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).

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?

Annotations declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, which align with the dispatch action. The description adds context about the specialist's approach and output structure, but does not detail potential side effects or authorization requirements.

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 with front-loaded purpose and bullet points. Every sentence adds value, covering use cases, exclusions, and output. Efficient and informative 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?

For a complex dispatch tool with no output schema, the description fully specifies what is returned (four-axis assessment, drivers, counter-signals, etc.) and covers limitations via NOT-for clauses. All critical usage context is provided.

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. The description does not add parameter-specific guidance beyond the schema, though the overall purpose helps contextualize the parameters.

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 it dispatches to a trend researcher for recency-dominant trajectory investigation, lists specific use cases (e.g., 'is X a real trend'), and distinguishes from siblings by explicitly naming alternatives (dispatch_desk_researcher, dispatch_market_analyst, 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?

Provides explicit when-to-use ('Use for: ...') and when-not-to-use ('NOT for: ...') with sibling tool names. Also describes the methodology ('Commits to falsifying conditions before searching'), offering clear guidance on invocation context.

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