cyclesite-mcp-server

save_search

Subscribe the user to alerts for new Cyclesite listings matching a filter — the AI assistant will then proactively notify them when a matching bike appears (price drop or fresh listing). Requires OAuth scope listings:read (read-only on data, but this is technically a write — it creates a SavedSearch row on the user's account). Examples: 'let me know when a Trek Domane SL 6 in Manchester under £2,000 appears', 'alert me to any e-MTB drops below £2,500 in Yorkshire'. Each user is capped at 50 active alerts.

Input Schema

TableJSON Schema

Name	Required	Description
`city`	No	UK city to focus on.
`name`	No	Optional human-readable name (e.g. "Trek Domane in Manchester").
`brand`	No
`model`	No
`category`	No
`maxPrice`	No
`minPrice`	No
`condition`	No
`alertFrequency`	No	How often to send digest. Default: instant.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedAug 4, 2026

changedInput schema / properties / category / enum

Previous value: -[
-  "road",
-  "mtb",
-  "gravel",
-  "hybrid",
-  "ebike",
-  "kids",
-  "bmx",
-  "folding",
-  "city",
-  "touring",
-  "triathlon",
-  "track",
-  "cyclocross",
-  "cargo",
-  "other"
-]New value: +[
+  "road",
+  "mtb",
+  "gravel",
+  "hybrid",
+  "ebike",
+  "kids",
+  "bmx",
+  "folding",
+  "city",
+  "touring",
+  "triathlon",
+  "track",
+  "cyclocross",
+  "cargo",
+  "adaptive",
+  "other"
+]

First observedMay 7, 2026

TDQS

A4.6/5.0

Behavior5/5

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

The description discloses the OAuth scope nuance ('listings:read' but technically a write), explains the creation of a SavedSearch row, and notes the 50-alert cap. It adds substantial context beyond the annotations, including the proactive notification behavior and the distinction between read and write semantics.

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 three sentences long, front-loaded with the main purpose, and every sentence adds value: purpose, auth nuance, examples, and cap constraint. No redundant or filler content.

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?

Given the output schema exists and the description covers purpose, auth, examples, and constraints, it is highly complete. However, it does not address behavior near the cap (e.g., error handling) or whether multiple identical alerts are allowed, which are minor gaps.

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 only 33%, but the description compensates with examples that map to parameters (brand, model, city, maxPrice, category). It does not exhaustively explain every parameter but provides enough grounding for filter construction.

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 uses a specific verb ('Subscribe') and a clear resource ('alerts for new Cyclesite listings matching a filter'), with concrete user-facing examples that illustrate the behavior. It clearly distinguishes from sibling search tools by emphasizing proactive notification rather than immediate results.

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 examples strongly imply when to use this tool (e.g., when a user asks to be notified about future listings), but it does not explicitly state 'use this instead of search' or provide exclusion criteria. The context is clear enough for an agent to infer the appropriate scenario.

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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