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by co.heista

Dispatch — market-analyst

dispatch_market_analyst

Dispatch to the MARKET ANALYST — entity-deep teardown of a named brand or vendor. Use for: "what is brand X / how does company Y work / decode competitor Z / teardown vendor W". Multi-axis extraction grounded in multi-class sourcing, plus defensible MOAT and credible GAP theses. Vertical and geography agnostic. Returns: 8-axis extraction (positioning / offer / audience / voice / pricing / distribution / proof / trajectory) + MOAT thesis + GAP thesis + Sources. NOT for: topic landscapes without a named entity (use dispatch_desk_researcher) / trajectory questions about a category (use dispatch_trend_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

A4.1/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false (may mutate), openWorldHint=true, idempotentHint=false. The description does not contradict these but adds minimal behavioral detail beyond stating it's a 'dispatch' tool that spawns a specialist. No contradiction detected.

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 well-structured with front-loaded purpose, use cases, exclusions, and return details. It is fairly concise but could be slightly trimmed without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 well-documented parameters and no output schema, the description adequately explains the tool's purpose, return structure, and differentiation from numerous siblings. Some behavioral details could be added, but overall complete.

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 description coverage is 100%, so baseline is 3. The description adds high-level context but does not enhance understanding of individual parameters beyond their schema descriptions.

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 performs an entity-deep teardown of a named brand or vendor, with specific examples of use cases. It explicitly distinguishes from siblings by noting what it is NOT for (e.g., dispatch_desk_researcher for topic landscapes, dispatch_trend_researcher for trajectory questions).

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 provides explicit when-to-use (e.g., 'what is brand X / how does company Y work') and when-not-to-use scenarios, naming alternative tools. This gives clear guidance for tool selection.

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