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dantalan

baatjie-mcp-server

by dantalan

Log BRM Sprint Activity

baatjie_log_activity

Record BRM outreach, demos, and sales per sprint block to measure progress toward daily goals and ensure visibility in baatjie_pipeline.

Instructions

Record a BRM's outreach, demos and sales for a 33/22 sprint block. This is how the daily 26/day objective gets measured — unlogged work is invisible to baatjie_pipeline.

Args:

  • brm_id (string): BRM identifier, must exist in tanOS brms

  • outreach (number): Contacts made in this block (default: 0)

  • demos (number): Demos booked or run (default: 0)

  • sales (number): Agencies signed (default: 0)

  • sprint_block (string, optional): Which block, e.g. 'sprint-3-build'. Defaults to the current 33/22 position from the clock.

  • activity_date (string, optional): YYYY-MM-DD, defaults to today

  • actor (string): Who is logging, for the audit trail

  • response_format ('markdown' | 'json'): Output format

Returns: { "ok": true, "activity": {...}, "clock": {...}, "audit_logged": boolean }

Examples:

  • End of a build block -> brm_id='marius-ai', outreach=12, demos=3

  • Backfill yesterday -> brm_id='...', sales=1, activity_date='2026-08-07'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actorNoWho is performing this write, recorded in tanOS audit_log (e.g. 'baatjie', 'marius-ai', 'deon-ai')mcp
demosNo
salesNo
brm_idYesBRM identifier
outreachNo
sprint_blockNo
activity_dateNo
response_formatNoOutput format: 'markdown' for human-readable, 'json' for machine-readablemarkdown
Behavior4/5

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

With all annotations false, the description carries the transparency burden. It discloses that brm_id must exist in tanOS brms, that actor is recorded in the audit trail, that sprint_block defaults to the current 33/22 position from the clock, and that the return includes an audit_logged boolean. These details go beyond what schema/annotations provide, though it does not discuss overwrite or concurrency behavior.

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 well-structured with an opening purpose, a concise Args list, a Returns shape, and practical examples. Every sentence adds value—there is no fluff, and the format is scannable.

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?

The description provides the return object structure, usage examples, and contextual linkage to baatjie_pipeline. It does not explain nested fields of 'activity' or error handling, but for a logging tool with 8 parameters and no output schema, this level of detail is sufficient for an agent to use it effectively.

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?

Schema description coverage is only 38%, but the description compensates by explaining each parameter: outreach, demos, sales, sprint_block, activity_date, actor, response_format, and brm_id. It provides defaults, formats, and examples, making all 8 parameters meaningful beyond the raw schema.

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 opens with 'Record a BRM's outreach, demos and sales for a 33/22 sprint block.' This is a specific verb+resource+scope statement. It clearly distinguishes the tool from siblings like baatjie_query or baatjie_pipeline by focusing on logging activity metrics.

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?

The description states 'This is how the daily 26/day objective gets measured — unlogged work is invisible to baatjie_pipeline,' which tells the agent when to use this tool and its downstream effect. Examples (end of build block, backfill yesterday) illustrate common scenarios. However, it does not explicitly contrast with alternative logging tools like baatjie_insert.

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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