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Platano78

Smart-AI-Bridge

get_analytics

Retrieve internal telemetry including backend invocation counts, success rates, latency distributions, cost estimates, and routing recommendations to diagnose backend selection and tune routing rules.

Instructions

Inspect SAB's internal telemetry: backend invocation counts, success/failure rates, latency distributions, estimated token spend per provider, and recent routing decisions. Read-only — never calls an LLM, never writes to disk. Use to diagnose 'why did SAB pick backend X', tune routing rules, or understand cost trade-offs across providers. Report types are cumulative: full_report includes everything from the other types. Returns: {success, report_type, data} where data depends on report_type — current: {backends:{[name]:{invocations, success_rate, p50_ms, p95_ms}}, session_uptime, timestamp}. historical: {time_range, series:[{timestamp, backend, calls, errors, latency}]}. cost: {by_backend:{[name]:{tokens_in, tokens_out, estimated_usd}}, total_estimated_usd}. recommendations: {recommendations:[{type, suggestion, confidence}]}. full_report: a merged object with all sections. If analytics hasn't initialized, returns {message, basic_stats:{uptime, memory, timestamp}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
report_typeNo`current` = stats since this server started (invocation counts, success rate, p50/p95 latency per backend). `historical` = time-bucketed series over `time_range`. `cost` = estimated token spend per backend, with cost-per-1K-tokens projections. `recommendations` = SAB heuristics on backend selection (e.g. "switch coding tasks to qwen3 — 18% faster on your traces"). `full_report` = all of the above.
time_rangeNoLookback window for `historical` and `cost` reports. Ignored for `current` and `recommendations`. Default: 7d.
formatNo`json` = machine-readable nested object. `markdown` = human-readable summary with tables. Default: json.
Behavior5/5

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

Despite no annotations, the description fully discloses behavior: read-only, no LLM calls, no disk writes. It explains cumulative report structure, return formats per report type, and the edge case when analytics hasn't initialized.

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 somewhat long but front-loaded with purpose and behavioral traits. Return type details are structured with bullet-like formatting. Minimal redundancy; each part serves a 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?

No output schema exists, but the description comprehensively documents return structures for each report type and the initialization error case. Covers everything an agent would need to invoke correctly.

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% and already details enum values. The description adds some examples (e.g., recommendation text) but doesn't significantly enhance understanding 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 inspects internal telemetry (backend invocations, success rates, latency, cost, routing decisions). It distinguishes from siblings like check_backend_health or analyze_file by focusing on SAB's internal analytics.

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?

Explicitly lists use cases: diagnose backend selection, tune routing rules, understand cost trade-offs. It also states it's read-only and never calls an LLM or writes to disk. However, it doesn't explicitly contrast with sibling tools for when not to use.

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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