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by co.heista

Dispatch — desk-researcher

dispatch_desk_researcher

Dispatch to the DESK RESEARCHER — source-grounded synthesis on a topic landscape. Use for: "what is known about X / give me the landscape of Y / fact-check Z / synthesize the published evidence on W". Multi-source FACT/INFERENCE extraction with citation discipline. Vertical and geography agnostic. Returns: BRIEF restatement + NOT IN SCOPE + findings with FACT/INFERENCE/SPECULATION labels + [n] citations + Sources block. NOT for: trajectory questions (use dispatch_trend_researcher) / entity teardowns (use dispatch_market_analyst) / numerical effect sizes (use dispatch_quantitative_researcher) / community quotes (use dispatch_qualitative_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

A4.6/5.0
Behavior5/5

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

The description discloses that the tool performs multi-source FACT/INFERENCE extraction with citation discipline, returns labeled findings, and is vertical/geography agnostic. Annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false) are consistent and the description adds valuable behavioral context beyond what annotations provide.

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?

The description is concise (~100 words), front-loaded with purpose, and efficiently covers use cases, exclusions, and return structure without unnecessary repetition.

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 the tool has 5 parameters and many siblings, the description provides a clear return structure (BRIEF + NOT IN SCOPE + findings + citations + Sources) and lists exclusions. It lacks mention of the async variant (dispatch_desk_researcher_async) but is otherwise 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 coverage is 100% with descriptions for all 5 parameters. The description does not add extra meaning to individual parameters beyond the schema, but the schema itself is sufficient. Baseline of 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 states the tool dispatches to a desk researcher for source-grounded synthesis, with specific use cases like 'what is known about X' and 'fact-check Z'. It distinguishes itself from siblings by listing four excluded tool types (e.g., dispatch_trend_researcher for trajectory), leaving no ambiguity about its function.

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 says 'Use for:' and 'NOT for:' with clear alternatives for each excluded use case. This gives the agent precise context for when to invoke this tool vs. its siblings.

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