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mshegolev

jaeger-mcp

by mshegolev

jaeger_get_dependencies

Read-onlyIdempotent

Retrieve service dependency graphs from Jaeger to analyze call patterns, identify high-traffic connections, and verify service topology within specified time windows.

Instructions

Retrieve the service-to-service call graph from Jaeger.

Wraps GET /api/dependencies. Returns directed edges (parent → child) with call_count — the number of spans where parent called child in the lookback window.

Use this to understand service topology, find high fan-out services, or verify that a new service is connected as expected.

Examples: - Use when: "What services does order-service call?" → check edges where parent='order-service'. - Use when: "Map the full service dependency graph for the last 7 days" → lookback_hours=168. - Use when: "Which services are called most frequently?" → sort edges by call_count descending. - Don't use when: You want detailed span timings (use jaeger_search_traces + jaeger_get_trace instead). - Don't use when: You need real-time data — Jaeger's dependency graph is aggregated and may lag by minutes.

Returns: dict with end_ts_us / lookback_hours / edge_count / edges (list of {parent, child, call_count}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_tsNoEnd timestamp in microseconds since Unix epoch UTC (optional). Defaults to now. Example: 1713400000000000.
lookback_hoursNoNumber of hours to look back from end_ts (1-720, default 24).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_ts_usYes
lookback_hoursYes
edge_countYes
edgesYes
Behavior4/5

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it discloses that the dependency graph is aggregated and may lag by minutes (not real-time), and it explains what the returned edges represent (parent → child with call_count). This enhances the agent's understanding of the tool's operational characteristics.

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 and front-loaded with the core purpose, followed by usage guidelines, examples, exclusions, and return format. Every sentence adds value without redundancy, and the bulleted lists improve readability while maintaining efficiency.

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 moderate complexity, rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, usage, behavioral nuances (e.g., data lag), exclusions, and return structure, providing all necessary context for an agent to invoke the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing full documentation of the two parameters (end_ts and lookback_hours). The description adds minimal parameter-specific semantics beyond the schema, such as example usage with lookback_hours=168 for 7 days, but does not significantly enhance the schema's information. This meets the baseline for high schema coverage.

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 specific action ('Retrieve the service-to-service call graph') and resource ('from Jaeger'), distinguishing it from sibling tools like jaeger_search_traces (for detailed span timings) and jaeger_list_services (for listing services). It explicitly mentions the API endpoint wrapped and the type of data returned.

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 provides explicit guidance on when to use this tool (e.g., for understanding service topology, finding high fan-out services, verifying new service connections) and when not to use it (e.g., for detailed span timings or real-time data). It names specific alternatives (jaeger_search_traces + jaeger_get_trace) for excluded use cases.

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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