Bike MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.4.0
Disambiguation5/5
Each tool targets a distinct operation: listing documents, retrieving outlines, creating documents, creating rows, grouping rows, updating rows, deleting rows, and querying. Even though create_document and create_rows both create structure, they operate at different levels (document vs. rows within a document), avoiding ambiguity.
Naming Consistency5/5
All tools follow the pattern 'bike_<verb>_<noun>' using imperative verbs (list, get, create, group, update, delete, query). The naming is uniform and predictable, with only minor plural/singular variation that does not hinder understanding.
Tool Count5/5
With 8 tools, the server is well-scoped for a focused outlining application. Each tool addresses a core functionality without unnecessary bloat, and the count feels natural for the domain.
Completeness4/5
The tool set covers essential CRUD operations for rows (create, read via outline/query, update, delete) and document lifecycle (create, list). Missing operations like explicit row reordering or document closing are minor and can be worked around via existing tools, making the surface largely complete.
Average 4.6/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on December 3, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and destructiveHint=false, so description carries burden. It describes that rows are moved under a parent and default placement behavior, but does not disclose side effects like removal from previous groups or impact on row ordering within the parent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is structured into sections for each mode and position, uses bullet-style clarity. Every sentence provides distinct information; only minor redundancy in explaining default placement.
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 full schema coverage and no output schema, the description adequately covers the two modes and placement options. It could elaborate on the effect on existing row hierarchy, but overall sufficient for an agent to use correctly.
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 100%, baseline is 3. Description adds value by explaining the relationship between group_name and parent_id for the two modes, and clarifying position defaults and requirements (e.g., reference_id needed for 'before'/'after').
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?
Clearly states the tool groups multiple rows under a parent, with two specific modes (create new group or move to existing). The verb 'group' and resource 'rows' are explicit, and it distinguishes from sibling tools like bike_create_rows or bike_update_row.
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?
Provides explicit conditions for using each mode: group_name for new group, parent_id for existing. Also explains position parameter usage. However, lacks guidance on when not to use this tool (e.g., when simpler reordering suffices).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description adds value by explaining the output format (active document marked with asterisk) and error conditions. It does not contradict 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?
The description is concise and well-structured with clear sections (Args, Returns, Examples, Errors). Every sentence provides value, and the key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters, the description fully covers functionality, prerequisites (Bike must be running), output format, examples, and error cases. Annotations provide safety context. No output schema exists, but the description adequately explains the return values.
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?
There are no parameters, and the input schema is empty. The description does not need to add parameter semantics. Per guidelines, 0 parameters baseline is 4. The description explains the return format, which is helpful but not directly parameter-related.
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 clearly states 'Lists all open documents in Bike' with a specific verb and resource. It distinguishes from sibling tools like bike_get_document_outline and bike_create_document, which have different purposes.
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 includes important usage context: 'Bike must be running for this tool to work.' It also lists error messages like 'Bike is not running' and 'No documents open', guiding when the tool can be used. However, it does not explicitly mention when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that HTML updates recreate the row, changing the row ID while preserving children, and recommends fetching the new ID via bike_get_document_outline. This adds value beyond annotations. However, the idempotentHint annotation is contradicted by the row ID change behavior on HTML updates.
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-organized into Args, Returns, Examples, and Errors sections. The first sentence states the core purpose. Examples are concise and illustrative, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter (updates) with nested structure, the description thoroughly covers its inner fields, return value (confirmation message), and error cases. No output schema exists, so the description appropriately provides the return format.
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 100%, but the description adds examples showing how each field is used, clarifies the meaning of html and type values, and explains constraints like the need for at least one of name or type per row.
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 clearly states the tool updates one or more rows' text content and/or type. It distinguishes from sibling tools (create, delete, query) through its action verb and examples showing modification of existing rows.
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?
Examples demonstrate various use cases (single update, batch to task, mixed updates, HTML). Error messages provide context for when the tool cannot be used. However, no explicit comparison to alternatives like bike_create_rows for when to create vs update.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses all relevant behaviors: creation with nesting, positioning, HTML support, and prerequisite errors. Annotations are all false, and the description does not contradict them, fully covering the tool's effects and requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns, Errors, Examples), but is somewhat verbose. However, every sentence adds value, and the examples are particularly helpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters and complex nested options, the description is thorough: it covers all parameters, provides examples for common use cases, and lists error conditions. No output schema exists, but the return format is adequately specified.
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?
While the input schema provides 100% description coverage for parameters, the tool description enhances understanding with detailed examples, nested structure explanations, and usage patterns for each parameter, adding value beyond the 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 clearly states it creates one or more rows with nested structure, using a specific verb and resource. It is distinct from sibling tools like update, delete, and group, establishing a unique purpose.
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 provides context such as optional parent_id, positioning options, and error conditions, but does not explicitly guide when to use this tool versus alternatives like bike_update_row. The naming and sibling list make usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, idempotent, non-destructive. Description adds that it returns a hierarchical tree with IDs and text, and explains behavior when max_depth is omitted. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections, but slightly verbose with examples and error messages. Front-loaded with main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without output schema, description includes sample output format, examples, and error cases. Complete for the tool's complexity.
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 covers the parameter fully (100%), and description adds context that omitting max_depth returns the full tree, which adds value beyond 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 clearly states it retrieves the outline structure of the currently open Bike document, using specific verb+resource. It distinguishes from siblings like bike_list_documents which lists documents, not outlines.
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?
Provides context that Bike must be running with a document open, and gives examples. However, it does not explicitly state when not to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral context beyond the destructiveHint annotation: deleted rows and children are permanently removed, undo only available via Bike's undo. This fully informs the agent of consequences.
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 sections for purpose, warning, args, returns, examples, and errors. Each sentence is necessary and front-loaded with key warning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description covers return value format and error messages. Annotations handle safety, schema handles parameter, description covers behavior and examples. Complete for a destructive operation.
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 100%, but the description adds meaning by explaining that row_ids is an array of IDs, children will be deleted, and provides examples of usage with specific IDs.
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 clearly states it deletes one or more rows from the document, using a specific verb ('delete') and resource ('rows'). This distinguishes it from sibling tools like create, update, and query.
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 includes a prominent warning about destructive nature and permanence, but does not explicitly state when to use this tool vs alternatives. The context of siblings implies usage, and examples help clarify.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds important behavioral details: return format (row text or scalar), error conditions, and prerequisites. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for syntax, args, returns, examples, and errors. It is slightly lengthy but justified by the query complexity. Front-loading the purpose and examples helps quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a query tool with one parameter and no output schema, the description covers syntax, return format, examples, and error prerequisites comprehensively. No critical gaps remain.
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 100% with a basic example. The tool description greatly expands meaning by detailing outline path syntax, advanced operators (union, intersect), and multiple examples, far exceeding the schema's brief note.
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?
Title 'Query Bike Rows' and description 'Search for rows using Bike's outline path syntax' clearly state the tool's function. Examples differentiate it from siblings like bike_create_rows.
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 provides explicit context with examples and error messages indicating prerequisites (Bike running, document open). It lacks direct comparison to alternatives like bike_get_document_outline but still guides usage effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=false, destructiveHint=false), the description discloses the return format ('Untitled (doc:XXX)'), behavior with/without structure, and error conditions. This adds valuable context for the agent.
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 Args, Returns, Examples, and Errors sections. It is concise yet comprehensive, front-loading the main purpose and using examples efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (2 params, nested structure, no output schema), the description covers all necessary aspects: parameter details, return value, error messages, and usage examples. This ensures the agent can use it correctly.
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?
With 100% schema coverage, baseline is 3, but the description elaborates on the nested structure of the 'structure' parameter, explains HTML formatting, and provides concrete examples, adding significant meaning beyond the 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 clearly states 'Creates a new document in Bike, optionally with an outline structure.' The verb 'create' and resource 'document' are specific, and it distinguishes from sibling tools like bike_list_documents or bike_get_document_outline.
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 provides clear context for when to use (creating documents), includes error messages like 'Bike is not running' for diagnostics, and gives examples. However, it doesn't explicitly state when not to use or mention alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![bike-mcp-server MCP server](https://glama.ai/mcp/servers/hildersantos/bike-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/hildersantos/bike-mcp-server)

Score Badge

Copy to your README.md:

[![bike-mcp-server MCP server](https://glama.ai/mcp/servers/hildersantos/bike-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/hildersantos/bike-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hildersantos/bike-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server