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Project Stack & Scripts

get_project_context
Read-onlyIdempotent

Answer what the user's project is — name, stack, how to run/test/build, auth, database, deploy, folder layout — from their files on disk, not from training data. ALWAYS call this before you invent npm/pip/cargo commands or read package.json yourself. ALWAYS call when the user says: what is this app, what's the stack, how do I run it, how do I test, is this a monorepo, where is auth, what database, how do we deploy. If they named Zephex or MCP, call this first on their project. One topic per call. Start with topic=identity on a new folder, then follow next_calls (usually run or framework). Other topics: backend, frontend, database, auth, deploy, structure, integrations, security. This is the user's machine, any project: Node, Python, Go, Rust, Java, PHP, a monorepo, an unsaved folder. Local/stdio: omit path to use the editor cwd, or pass path as their project folder. No disk on this transport: inline_files with package.json or pyproject.toml/go.mod/Cargo.toml plus 2–4 source files. Returns topic, summary, data (identity, commands, key_paths), hint, next_calls. Copy dev/test/build from data — do not guess bun vs npm vs uv. Not for finding a function name (find_code) or reading a file body (read_code). Those come after you know what the project is. Example: get_project_context({ topic: "identity" }) then get_project_context({ topic: "run" }). Also call topic=auth before touching login, topic=database before schema work, topic=structure when you need the folder map. force:true if the project just changed. Brief is enough for orientation; do not skip this tool to save a round-trip — one identity call replaces reading several manifests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoThe user's project folder. Local/stdio: omit to use editor cwd, or pass the absolute folder (any OS). Hosted with no disk: omit and use inline_files.
forceNoSet true to re-detect even if cached (use when project changed)
topicNoWhich slice to return (one per call). identity=project name/type + which topics apply; run=dev/test/build/lint commands; framework=language/runtime/package manager; backend=API routes and server entry points; frontend=UI framework and pages; database=ORM and providers; auth=login/session providers; deploy=hosting and CI; structure=monorepo layout; integrations=Stripe/Sentry/etc.; security=env and auth surface.
focus_onNoSubdirectory to focus the file tree scan on (e.g. 'src/tools')
detail_levelNoOutput tier: "brief" (default, ≤500 tokens), "standard" (full fields), "full" (all fields + file tree)
inline_filesNoPrimary way to supply code. Shape: { "": "", ... }. The VALUE is the actual file body — never a filename, path, or placeholder. Example: { "package.json": "{\"name\":\"my-app\",\"dependencies\":{...}}" }. Always include the project-definition file (package.json / pyproject.toml / Cargo.toml / go.mod / pom.xml / Gemfile / composer.json / pubspec.yaml) plus tsconfig.json / framework config if present, plus 2-4 representative source files. Works on Mac, Windows, Linux, private repos, unsaved folders.
structure_depthNoMax folder depth for file tree scan (default: 3, max: 6)
include_structureNoWhen true, includes file tree in response (also triggered by detail_level: full)

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / path / description
      Previous value: -"Absolute path to the project root. Stdio MCP reads manifests from disk directly. Omit on stdio when editor cwd is the project root."New value: +"The user's project folder. Local/stdio: omit to use editor cwd, or pass the absolute folder (any OS). Hosted with no disk: omit and use inline_files."
  2. Changed2 schema fields changed
    • changedInput schema / properties / path / description
      Previous value: -"Where the project lives. Accepts BOTH: • Local absolute path: /Users/alice/myapp, C:/Users/alice/myapp, /mnt/c/Users/alice/myapp. Local stdio install reads files directly. • GitHub/GitLab/Bitbucket URL: https://github.com/owner/repo (or short-form github:owner/repo). Hosted server fetches and analyses the repo (private GitHub repos work when the server has GITHUB_PAT or the user has linked GitHub). Use a remote URL whenever the user pastes a GitHub link or refers to their repo by name."New value: +"Absolute path to the project root. Stdio MCP reads manifests from disk directly. Omit on stdio when editor cwd is the project root."
    • changedInput schema / properties / topic / description
      Previous value: -"Named slice (each returns different data): identity | framework | backend | frontend | database | auth | deploy | run | structure | integrations | security. Local path or github:owner/repo — same topics. Call identity first; backend vs frontend vs auth return different summaries and key_paths."New value: +"Which slice to return (one per call). identity=project name/type + which topics apply; run=dev/test/build/lint commands; framework=language/runtime/package manager; backend=API routes and server entry points; frontend=UI framework and pages; database=ORM and providers; auth=login/session providers; deploy=hosting and CI; structure=monorepo layout; integrations=Stripe/Sentry/etc.; security=env and auth surface."
  3. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant context: data comes from disk (not training), one topic per call, caching with force flag, transport-specific instructions (omit path for local, use inline_files for hosted), and that commands should be copied from data without guessing. No contradiction with annotations.

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 lengthy but every sentence earns its place given the tool's complexity (8 params, multiple topics, different transports). It is front-loaded with core purpose and critical 'ALWAYS call' rules. Slight verbosity from examples and repetition, but justified by preventing misuse.

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?

With 8 parameters, no output schema, and annotations present, the description covers all bases: purpose, usage triggers, parameter semantics, return fields (topic, summary, data, hint, next_calls), caching behavior, transport handling, and disambiguation from siblings. It is fully complete for an AI agent to select and invoke 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?

Schema coverage is 100% with descriptions for all 8 parameters. The description adds substantial meaning beyond schema: explains topic values in detail (identity, run, framework, etc.), provides usage examples (get_project_context({ topic: 'identity' })), clarifies inline_files format with explicit example, and ties parameters together in a workflow (start with topic=identity then follow next_calls).

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 answers project identity, stack, and run/test/build commands from disk, not training data. It explicitly distinguishes from siblings like find_code (function name) and read_code (file body), providing clear differentiation.

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

Usage Guidelines5/5

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

The description gives strong usage guidance: 'ALWAYS call this before you invent npm/pip/cargo commands or read package.json yourself.' It lists trigger phrases, specifies topics order (start with identity), and warns when NOT to use (for finding functions or reading files). Explicit alternatives are named.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct domain: URL auditing, package checking, tests, architecture, code search, project context, planning, memory, code reading, and expert guides. Descriptions are extremely detailed and explicitly state when not to use each tool, leaving no ambiguity.

Naming Consistency4/5

Most tools follow the verb_noun snake_case pattern (audit_headers, check_package, check_test, explain_architecture, find_code, get_project_context, read_code), but project_memory (noun_noun) and Zephex_dev_info (brand_noun) deviate, and keep_thinking uses a gerund instead of a noun. The pattern is strong but not perfectly uniform.

Tool Count5/5

10 tools is well-scoped for a comprehensive development assistant. Each tool serves a clear purpose without redundancy, covering security, package management, testing, code understanding, project context, planning, memory, and expert knowledge. The count is neither too few nor excessive.

Completeness4/5

The tool surface covers a wide range of development analysis tasks: security auditing, package checking, test running, architecture mapping, code search, project context, planning, memory, code reading, and developer guides. Minor gaps include the lack of direct code editing or project execution/build tools, but the server appears intentionally focused on read-only information and planning.

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