Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@waymark show me the concept for src/main.rs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server exposing a codebase's OKF
knowledge bundle (okf/) to Claude Code — three schema-enforced query
tools instead of raw Read/Grep over hand-written docs.
list_concepts(type?, tags?) — frontmatter-only summaries. tags matches
any-of the given tags.
read_concept(path) — full frontmatter + body for one concept, by the
path a list_concepts call returned.
find_concept_by_resource(file_path) — reverse lookup: which concept(s)
describe a given source file.
The server is stateless — it re-reads okf/ from process.cwd() on every
call, so there's nothing to invalidate when concept files change.
Related MCP server: claudecode-mcp
npx -y @aleburrascano/waymark initThis registers everything a repo needs in one step:
Adds a waymark entry to .mcp.json (creating it if missing).
Installs a pre-commit hook at .git/hooks/pre-commit that blocks commits
when a resource-mapped file changes without its okf/ concept being
updated. Safe to re-run — it upgrades its own hook on later waymark
versions but never overwrites a hook it didn't install.
Installs two skills to .claude/skills/: okf-staleness-fix (the
writer/judge playbook Claude Code uses to resolve a blocked commit) and
okf-bootstrap (the propose/approve/generate/judge playbook Claude Code
uses to seed an initial okf/ bundle).
Adds (or refreshes) an ## OKF context section in the repo's CLAUDE.md.
Run it again any time to pick up updates from a newer waymark version.
okf/ bundles don't have to be hand-written from scratch. After running
waymark init, ask Claude Code to bootstrap one:
"Bootstrap okf for this repo"
This invokes the okf-bootstrap skill, which:
Runs waymark bootstrap — a deterministic scan (git ls-files-based,
respects .gitignore) that lists every candidate source file.
Proposes a candidate concept list grouped by domain (one concept per named unit you'd naturally ask "what is X" about) — nothing is written yet.
Waits for your approval, or feedback to re-group/rename/split candidates.
Generates each approved concept, verified by a judge subagent before it's written to disk.
Stages the new files with git add and reports what was generated —
committing is left to you.
This is a one-time, human-supervised pass. Ongoing drift after that is
caught by the pre-commit hook and okf-staleness-fix skill, not by
re-running bootstrap.
npm install
npm test
npm run build && npm link # then `waymark` runs the built CLInpm test runs vitest against TypeScript source directly — no build step
required for the test suite.
Find the OKF concept(s) whose "resource" field matches a given source file path — reverse lookup from code to the concept describing it.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Source file path relative to the repo root, e.g. "src/api/playback.ts" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavior. It discloses the matching logic (resource field match) and that results may be plural ('concept(s)'), but it doesn't describe exact-match rules, case sensitivity, handling of multiple/none matches, or output format. This leaves some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded, and every clause adds value. The example is in the schema, not duplicated here.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (1 required param) and the description explains its purpose and result type. However, without an output schema or annotations, it could more explicitly describe the return value (e.g., list of concept IDs vs full objects) and no-match behavior, so it's not a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the single parameter `file_path` with a clear description and example (100% coverage). The tool description adds no parameter-specific meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'find' and identifies the resource ('OKF concept(s)') and the matching criterion ('resource' field matches a source file path). The 'reverse lookup' phrasing clearly distinguishes it from the sibling list_concepts and read_concept tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly states the scenario: you have a source file path and want the concept describing it. It doesn't explicitly discuss when not to use it or mention alternatives, but the reverse-lookup framing makes the use case unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List OKF concept summaries (frontmatter only) from this repo's okf/ bundle, optionally filtered by type and/or tags.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Return concepts matching any of these tags | |
| type | No | Exact frontmatter type to filter by, e.g. "API Endpoint" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that only frontmatter summaries are returned (not full content), that the source is scoped to this repo's okf/ bundle, and that filtering is optional. This is meaningful behavioral disclosure for a read-only list operation, though it does not mention pagination or exact response shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the action, resource, scope, and filter options. Every word earns its place; there is no redundancy or filler. This is ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with two optional filters and no output schema, the description is adequately complete. It specifies the source bundle, the nature of the returned data (summaries/frontmatter), and the filter capability. It does not detail the exact return structure, but 'list' and 'summaries' imply a collection of summary objects, which is sufficient for an agent to invoke correctly. A slight gap is not explicitly stating that no filters returns all concepts, but this is inferred.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions already cover both parameters (tags and type) with clear semantics: tags match any, type is exact frontmatter type. The description adds the high-level 'optionally filtered by type and/or tags' but does not provide new meaning beyond what the schema already states. With 100% schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists OKF concept summaries from the repo's okf/ bundle, with the specific scope of 'frontmatter only.' This verb+resource+scope makes it distinct from sibling tools like read_concept (which reads an individual concept) and find_concept_by_resource (which searches by resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when you need summaries/frontmatter from the okf/ bundle, possibly filtered by type/tags. It does not explicitly name alternatives or exclusions, but the context is clear enough to infer that full content would require read_concept. This is clear context without explicit exclusions, scoring a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the full frontmatter and body of one OKF concept by its path (relative to the repo root, as returned by list_concepts).
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Concept path, e.g. "okf/api/playback.md" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosure. It appropriately conveys that this is a read operation (as the verb 'read' implies) and specifies exactly what content is returned (full frontmatter and body). It also explains the path semantics, which adds useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence. It leads with the core action and resource, then provides the key qualifier about path provenance, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read tool, the description is complete. It explains what is read, the path format, and the source of the path. Since there is no output schema, noting that it returns frontmatter and body satisfies the need for return-value clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the single path parameter with an example. The description goes further by specifying that paths are relative to the repo root and are returned by list_concepts, adding meaning beyond the schema's example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('read') and the resource ('one OKF concept'), including the scope ('full frontmatter and body'). It also distinguishes this tool from siblings by specifying the path-based access, which complements list_concepts and find_concept_by_resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context that this tool should be used when you have a path, which is explicitly tied to the output of list_concepts. It does not explicitly say when not to use find_concept_by_resource, but the path-centric wording implies the alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0find_concept_by_resourcelist_conceptsread_conceptScored across 3 tools
Each tool targets a distinct operation: listing summaries, reading a specific concept by path, and reverse lookup by resource. No overlaps or ambiguity.
All tools follow a consistent verb_noun pattern (list_, read_, find_) with snake_case. The naming is uniform and predictable.
3 tools is a minimal but reasonable scope for a read-only concept bundle server. It's slightly on the lean side but not inappropriately so.
Covers the core read workflows: list all concepts, read a specific one, and reverse lookup by resource. Lacks create/update/delete, but that may be outside the intended purpose.
Repository knowledge graph MCP server for codebase understanding and debugging.
Augments MCP Server - A comprehensive framework documentation provider for Claude Code
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
