Skip to main content
Glama

jambavan_context

Retrieves relevant code symbols and snippets from a codebase index, returning a token-budgeted context block of matching functions, classes, and types for efficient model use.

Instructions

Search the Jambavan index for code symbols and snippets most relevant to a query. Returns a ranked, token-budgeted context block of matching functions, classes, and types. Inject this block into your prompt to give the model precise, token-efficient codebase knowledge. Much cheaper than reading whole files — only the relevant symbol bodies are returned. Automatically includes top project-memory matches and a bounded set of extracted callers/callees under the same token budget. Options: compress_prose shrinks comments for extra budget; include_diff adds recent git changes; include_tests shows test coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax lexical symbols before token-budget truncation; up to 6 extracted structural neighbors may be added (default: 30)
queryYesNatural-language or identifier query — e.g. "auth middleware", "UserService.login"
include_diffNoInclude recent git changes for each symbol (default: false)
include_testsNoInclude associated test file info for each symbol (default: false)
compress_proseNoCompress comments/docstrings in results for more symbol density (default: false)
Behavior4/5

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

With no annotations, the description fully explains the tool's behavior: ranked results, token-budgeted context, automatic inclusion of project-memory and neighbors, and options affecting output. It discloses read-only nature and efficiency advantages without 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 a single, well-structured paragraph that front-loads the purpose and progressively details output, usage, and options. It could be slightly tighter (e.g., merging the injection advice) but is efficient and informative overall.

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 no output schema and 5 parameters, the description covers behavior adequately: explains output type, automatic inclusions, and options. Minor gaps include default token budget size and handling of empty results, but overall it provides sufficient context for an AI agent to use the tool.

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 value by explaining how parameters interact within the token-budget mechanism (e.g., limit, compress_prose, include_diff). It clarifies the automatic inclusion of neighbors and project-memory matches, enriching parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches the Jambavan index for code symbols/snippets relevant to a query and returns a token-budgeted context block. It implies differentiation from siblings by mentioning unique features (project-memory matches, callers/callees) but does not explicitly distinguish from similar search tools like jambavan_memory_search.

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

Usage Guidelines3/5

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

The description provides implicit usage guidance (inject into prompt, cheaper than reading whole files) but lacks explicit when-not-to-use conditions or direct comparisons to sibling tools. Users must infer usage from context.

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

Install Server

Other Tools

Latest Blog Posts

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/beingmartinbmc/jambavan'

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