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beesknees-mcp

Beesknees Match State

beesknees_match_state

The live board — every hive, every bee, and when to ask again.

Poll this rather than guessing a cadence: the answer carries poll_after_ms, computed from how hot the match actually is. One algorithm, owned here, so sixty clients throttle themselves and the server keeps a valve it can turn.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
npubNoRequired. Your Nostr public key (npub1...) for credit billing.
match_idNoThe match to read. Omit for the live one.
since_seqNoOnly answer if the board has moved past this.
dpop_tokenNo
next_roundNoYou have finished reading your last result — answer with the lobby that is forming rather than the round you just played.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden, and it does disclose one important trait: the response carries poll_after_ms computed server-side, and there is a server-side throttle valve. It stops short of operational detail — credit billing, rate limits, or what a stale since_seq does — so the disclosure is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Core intent is front-loaded in the first clause, which is good, but the prose is heavily metaphorical ('every hive, every bee', 'sixty clients throttle themselves and the server keeps a valve it can turn'). The last sentence is evocative rather than informative, costing clarity without adding actionable content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values needn't be explained, and 80% of parameters are documented in the schema. The description covers the polling contract but leaves the match_id/next_round/since_seq selection logic entirely to the schema, and the opaque vocabulary weakens an otherwise adequate picture.

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 80%, so the schema already documents npub, match_id, since_seq and next_round. The description adds no per-parameter meaning (its 'when to ask again' refers to the poll_after_ms output, not an input), so it earns the baseline 3.

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

Purpose3/5

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

The name plus 'the live board' imply this returns live match state, and the second sentence frames the action as polling. However, the metaphor ('every hive, every bee') never states the verb/resource plainly, and it doesn't distinguish itself from the sibling beesknees_match_list, so the purpose is inferred rather than declared.

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

Usage Guidelines3/5

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

'Poll this rather than guessing a cadence' gives a clear directive on when to use the tool and why. But it offers no named alternatives or exclusions (e.g. match_list vs this), so an agent still has to infer the boundary between this and other match tools.

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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