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pos-buzz

posbuzz-mcp

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by pos-buzz

start_run

Start a social analysis run in a workspace. Set keyword-source pairs, optionally product/competitor ASINs to correlate with sales.

Instructions

Starts an analysis run in the given workspace. This is the entry point of the workflow. Save data.token from the 202 response, wait for succeeded via read_run_status, then call the result endpoints.

  • social_queries: one row per (keyword, source) pair. Repeat rows to search multiple keywords and/or platforms.

  • Set product_id / competitor_asins to enable correlation between social metrics and EC sales (read_ec_results owned / competitor). Omit for social-only analysis.

  • If a run is already running for the tenant, returns 429 (one concurrent run max). Wait for the prior run to finish.

  • Invalid conditions (e.g. a bad date) return 422.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNoEnd date of the analysis period (YYYY-MM-DD).
date_fromNoStart date of the analysis period (YYYY-MM-DD).
product_idNoYour product ID. Set to enable correlation between social metrics and EC performance (read_ec_results owned). Optional.
workspace_idYesTarget workspace ID (from create_workspace / list_workspaces).
social_queriesNoOne row per (keyword, source) pair. Repeat rows to search multiple keywords and/or platforms.
competitor_asinsNoArray of competitor Amazon ASINs. Used to fetch the competitor dataset (read_ec_results competitor). Optional.
japanese_posts_onlyNoOptional. Defaults to "1"; keep only posts detected as Japanese from post text or hashtags after fetching.1

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesAn analysis job. Identified by token; result endpoints become available once status is succeeded.
Behavior5/5

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

With no annotations, the description carries full burden and exceeds expectations. It discloses async behavior (202 response with token), error codes (429 for concurrent run, 422 for invalid conditions), and workflow steps. This is rich behavioral context beyond what is structured elsewhere.

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 compact, front-loaded with the core purpose, and uses bullet points for additional guidance. Every sentence earns its place without redundancy or fluff.

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 run, error handling, optional correlation), the description covers workflow, error statuses, and parameter intent. With an output schema present, the description is sufficiently complete for an agent to invoke and manage the run correctly.

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

Parameters4/5

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 adds meaningful extra context for product_id/competitor_asins (enabling correlation) and social_queries row semantics, which helps agents decide parameter usage beyond 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 'Starts an analysis run in the given workspace' and positions it as 'the entry point of the workflow', distinguishing it from sibling read/import tools. The verb+resource pairing is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides a clear workflow sequence (save token, wait for status, call result endpoints) and explains conditional use of parameters (e.g., omit product_id for social-only). While it lacks an explicit 'use X instead' statement, the context effectively guides tool selection among 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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