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AGL Agency Capacity

Add anonymous numbers to the benchmark

add_to_benchmark

Adds 1 anonymous response to the Agency Capacity Benchmark run by Agile Growth Labs. Only call when the user explicitly agrees to share their numbers. Stores no name, email, or company. Max 3 per day per network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleYes
consentYesMust be true. The user agreed to add anonymous numbers.
bottleneckYesThe biggest delivery bottleneck.
revenue_bandYes
active_clientsYes
ai_tools_countYesAI tools used in client work.
account_managersYes
rebrief_hours_per_weekYesTeam hours per week spent re-briefing AI tools.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Annotations only cover the safety profile (non-read-only, non-destructive, non-idempotent, closed-world); the description goes well beyond them by disclosing anonymity (no name, email, or company stored), a concrete rate limit (max 3 per day per network), and the consent precondition. This is exactly the extra behavioral context an agent needs before committing a write.

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?

Three short sentences, front-loaded with the core action, followed in order by the consent gate, the anonymity guarantee, and the rate limit. No filler or redundancy.

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?

For an 8-required-parameter write tool with no output schema, the description covers the decision-critical facts (consent, anonymity, quota). It omits any statement about failure handling, duplicate submission behavior, or what the collected numbers feed into, which are minor but real gaps.

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 50%, and the description adds no field-level meaning for role, revenue_band, active_clients, or account_managers, whose enum/integer semantics must be inferred from the schema alone. It does reinforce the consent parameter ('only call when the user explicitly agrees'), but that duplicates the schema's own 'Must be true' note.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource (adds one anonymous response to the Agency Capacity Benchmark) and identifies the operator (Agile Growth Labs), so the agent knows exactly what is written. It does not explicitly contrast itself with siblings like capacity_benchmarks or capacity_check, leaving that distinction to inference.

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?

Gives an explicit gating condition: 'Only call when the user explicitly agrees to share their numbers,' which is exactly the precondition an agent needs before invoking. No alternatives or when-not-to-use cases are named beyond that, and no sibling is referenced.

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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