Skip to main content
Glama

get_stats

Check how much knowledge is stored — fact counts, entities, domains, embedding coverage, and token usage — to verify memory health and recall gaps.

Instructions

Get knowledge base statistics — how many facts are currently true, how many are held in total including superseded history, entity and domain counts, how facts are distributed across domains, and how much raw log can be reclaimed.

Call this when the user asks what you know or remember about them, how much you have stored, or whether their memory is working. This answers "how much do you know", not "what do you know" — use search_knowledge, get_entity or get_context for actual recall.

embeddings reports semantic-search coverage per model. An empty list means this store searches by keyword only, which is the default. A count well below the current fact count means some facts are findable by wording but not by meaning — worth mentioning if the user asks why something was not recalled.

extract.unextracted_events is how many transcript lines extract has not examined. pending_facts is I not yet integrated. A large unextracted count with a healthy fact count means capture is writing D that extract has not examined.

intelligence is billed consolidation spend (calls, tokens, elapsed), broken down by stage and provider for the last 24 hours, all time, and the last few runs. Embeddings are not this number — they are a separate API. Token fields are omitted when the provider did not report them, not shown as zero.

token_budget is remaining room under optional intelligence.token_budget caps (per billed provider, rolling hour / day / week / month). Unset means unlimited. Over the cap, consolidate skips extract, holds the watermark, and does not fall back to the heuristic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.30.1

TDQS

A4.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral transparency, but it never explicitly states that this tool is read-only and has no side effects. It clarifies the meaning of fields and that token fields are omitted rather than zero, but it stops short of confirming that calling get_stats does not modify data or trigger background processes.

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 longer than strictly necessary, but the extra detail is purposeful and well structured with inline-code field names and separate paragraphs for each metric. It front-loads the main purpose and then handles potential confusions like embeddings versus intelligence, which justifies the length.

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?

Since there is no output schema, the description provides a thorough explanation of the statistics returned, including edge cases such as omitted token fields, unlimited token budgets, and behavior over the cap. This gives enough context for an agent to understand what the results mean without needing a formal output schema.

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?

The tool has zero parameters and the schema is complete, so there are no parameter semantics left unexplained. The baseline for zero parameters is 4, and the description correctly adds no irrelevant parameter information.

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 a specific verb and resource: 'Get knowledge base statistics' and enumerates exactly which statistics are returned. It also differentiates the tool from siblings like search_knowledge, get_entity, and get_context, making its purpose unmistakable.

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?

It explicitly says when to call the tool: when the user asks what is known, how much is stored, or whether memory is working. It also names alternatives for actual recall, saying to use search_knowledge, get_entity, or get_context, which gives clear when-not-to-use guidance.

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