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boardsesh_recommend_climbs

Read-only

Find new climbs to try from your Boardsesh logbook. Get personalized recommendations by grade, popularity, recency, or hidden gems, filtered by board and angle.

Instructions

Suggest climbs the user has not sent yet, from Boardsesh's recommendations.

Use when the user asks what to try next, wants new climbs, projects at their level, or something fresh. 'at_level' works from their own send history and excludes climbs they have already sent. Needs a board configuration, so it uses one of the user's saved Boardsesh boards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo'at_level' picks climbs around the user's current grade, 'crowd_favorites' popular ones, 'hidden_gems' under-climbed ones, 'fresh' recently set ones.at_level
angleNoWall angle in degrees (e.g. 20, 40). Omit for all angles.
boardNoBoard type to filter to: kilter, tension, moonboard, decoy, touchstone, soill, grasshopper, woods or spray. Omit for all boards.
limitNoMaximum number of items to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Beyond the readOnly/openWorld annotations, the description reveals that at_level derives from the user's send history, excludes already-sent climbs, and falls back to a saved board configuration. This gives useful behavioral context without contradicting annotations.

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-load the purpose, then add usage triggers and the one key behavioral prerequisite. There is no filler or repetition.

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?

With an output schema and safe read-only annotations, the description covers the main triggers, the at_level behavior, and the prerequisite that a saved board is used. It could add one line about how other kind values treat send history, but this is not essential for correct invocation.

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 already complete, so the baseline is 3, and the description adds extra meaning for at_level by explaining it uses send history and excludes sent climbs, plus the saved-board dependency. It does not over-explain the other parameters, which the schema already documents.

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 a specific action and resource: suggest climbs the user has not sent yet from Boardsesh recommendations. It distinguishes this from sibling search/project tools by emphasizing recommendations and unsent climbs.

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 gives explicit trigger contexts such as asking what to try next, wanting new climbs, projects at their level, or something fresh. It also states the board-configuration prerequisite, though it does not name alternative siblings or say when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.