Ingest local RDF files (ttl/nt/nq/rdf/owl/jsonld/json) into a named graph. Returns a job_id; runs in the background — poll with brainkb_job_status.
| Name | Required | Description | Default |
|---|---|---|---|
| file_paths | Yes | ||
| max_concurrency | No | ||
| named_graph_iri | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the operation runs in the background, returns a job_id, and requires polling via brainkb_job_status. This is valuable behavioral information beyond the basic 'ingest' verb, though it does not mention potential side effects or prerequisites.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long and immediately states the tool's purpose. Every part adds value: the action, the formats, the target, and the follow-up polling step. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, no output schema, and no annotations. The description adequately covers the core workflow: what to provide (files, graph) and what to expect (job_id, background execution, polling). It does not detail max_concurrency or job status semantics, but for a relatively straightforward ingest tool, it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It clarifies that file_paths are local RDF files with specific formats and that named_graph_iri is the target graph. However, max_concurrency is not mentioned at all, leaving its purpose (parallelism control) implied only by its name and default value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Ingest local RDF files') and the target resource ('into a named graph'). It lists supported formats, which adds specificity, and the phrasing distinguishes it from sibling tools like brainkb_ingest_text by focusing on files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on how to use the tool: it mentions local RDF files, a named graph, and explicitly instructs to poll with brainkb_job_status after receiving a job_id. It does not explicitly state when not to use it or name alternatives, but the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, such as user management (activate, ban, assign role) vs. space management (create, add member, set visibility) vs. ingest/jobs (ingest_files, job_status, recover). A few pairs like grant_capability vs. grant_role_capability are similar but descriptions clarify the target, so an agent should be able to choose correctly.
All tools are prefixed with brainkb_ and the large majority follow a verb_noun pattern (e.g., add_space_member, list_tokens, revoke_token). Some exceptions like brainkb_delta, brainkb_search, brainkb_whoami, and brainkb_capabilities break the pattern, but these are few and still readable.
With 49 tools, the server is far above the 25+ threshold for 'too many'. While the broad scope (user admin, spaces, graphs, ingest, provenance, auth) justifies many operations, the sheer number makes it heavy and potentially unwieldy for an agent to navigate.
The tool set covers a wide range of use cases: user/role/capability management, space administration, graph registration, ingest, job monitoring, provenance, search, and SPARQL. However, there are notable gaps such as removing a space member, deleting a space, or updating space metadata, which could leave agents without a way to fully manage the lifecycle of a space.