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NimbleBrainInc

synapse-research

start_research

Run a deep research task on a query. Optionally anchor on provided seed text or file URI to receive a markdown report.

Instructions

Run a research task on the given query. Supports MCP task augmentation — clients that advertise tasks.requests.tools.call may wrap the request with a task field to receive a CreateTaskResult and poll via tasks/get; clients that do not will block until the research completes and receive the full report inline. Either way, the server creates a research_run entity and updates its progress in real time so the Synapse UI can render live status. The worker typically takes ~60 seconds to 3 minutes and returns a markdown report. Pass title (3-8 word label) when you already know the topic — saves a background LLM call the server would otherwise make to derive one. Two ways to anchor research on existing material (use exactly one): seed_uri is a files:// URI the server reads itself via the host's ai.nimblebrain/host-resources extension — preferred when the host advertises the extension, since the agent doesn't have to load file bytes through its own context. seed_data is raw text passed inline by the agent — the universal fallback that works on every host. If the host doesn't support host-resources and seed_uri is provided, the tool returns a clear error telling the agent to retry with seed_data instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe research query or topic.
titleNoOptional short label (3–8 words) for list rows and the detail-view heading. When omitted, the server generates one in the background via the FAST_LLM and patches the entity ~500ms later.
seed_uriNoOptional `files://<id>` URI pointing at a workspace file the research should anchor on. Resolved server-side via the `ai.nimblebrain/host-resources` extension. Prefer this when the host advertises the extension — saves the agent's context budget.
seed_dataNoOptional raw text the research should anchor on. Universal fallback that works on hosts without the host-resources extension. Mutually exclusive with `seed_uri`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description fully covers behavioral traits: the tool creates a 'research_run' entity, updates progress in real time, typical execution time (60s to 3 min), and return format (markdown report). It also explains the side effects of the 'title' parameter and the fallback logic for seed parameters.

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 fairly long but front-loaded with the main purpose. Every sentence adds value given the tool's complexity, though it could be slightly more concise by combining some details. Still well-structured with clear separation of topics.

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, two modes, multiple parameters with fallback logic) and the existence of an output schema, the description provides complete context. It covers all necessary details for an agent to correctly select and invoke the tool, including practical tips and error scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds significant meaning beyond the schema. It explains why 'title' is optional (saves a background LLM call), the trade-offs between 'seed_uri' and 'seed_data' (resource extension vs universal fallback), and the error handling for 'seed_uri' when the host doesn't support the extension.

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 verb 'Run' and the resource 'research task on the given query'. It distinguishes itself from sibling tools (get/list/search/delete) by being the only tool that initiates research. The explanation of both blocking and task augmentation modes further clarifies its purpose.

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 guidance on when to use the tool, including two execution modes (MCP task augmentation vs blocking), advice on using the 'title' parameter to save a background LLM call, and clear instructions for choosing between 'seed_uri' and 'seed_data' based on host capabilities. It also tells the agent to retry with 'seed_data' if 'seed_uri' fails.

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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