Skip to main content
Glama

brainkb

by org.brainkb

brainkb_ingest_text

Ingest raw RDF text (Turtle / N-Triples / JSON-LD, auto-detected) into a named graph. Returns a job_id; ingestion runs in the background — poll with brainkb_job_status. The graph must be registered (see brainkb_add_space_graph) and the caller must have write access to its space.

`sha256` / `expected_bytes` are an integrity contract, and you should use them
whenever the RDF came from a file. Ingest is append-only — no delete for
triples, no unregister for a graph — so RDF that arrives here mangled is
permanent. Because `data` is a string, it passes through the caller's context,
where dense Turtle is exactly what gets silently altered: ligatures, Greek
letters, embedded newlines, escaped quotes. Declare the digest of the bytes you
MEANT to send (`shasum -a 256 file.ttl`) and this refuses the write on any
mismatch, turning an unrecoverable corruption into a clean rejection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
sha256No
expected_bytesNo
named_graph_iriYes

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description excels at disclosing behavior: background execution with job_id, append-only semantics, permanence of mangled data, and the risk of silent corruption through string context. It also explains the integrity contract and clean rejection on mismatch, providing exceptional transparency.

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 dense but every sentence adds critical information: purpose, return value, prerequisites, integrity contract, and hazards. It is well-structured, starting with the core action, then contextual details, and is appropriately sized for the tool's complexity.

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?

Despite no output schema or annotations, the description covers purpose, return value, job polling, prerequisites, append-only behavior, integrity checks, and parameter guidance. It references related tools and would allow an agent to invoke this tool safely and effectively.

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

Parameters5/5

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

Schema has 0% description coverage, but the description richly explains the semantics of sha256 and expected_bytes as an integrity contract, warns about data as a string, and implies named_graph_iri via 'named graph' and registration. This fully compensates for the schema's lack of descriptions.

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 tool ingests raw RDF text (Turtle/N-Triples/JSON-LD) into a named graph, with specific verb and resource. It distinguishes itself from sibling tools like brainkb_ingest_files and brainkb_ingest_upload by focusing on text input and highlighting background processing.

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?

Provides clear context: prerequisites (registered graph, write access), how to track progress (poll brainkb_job_status), and when to use integrity params (when RDF came from a file). Does not explicitly name alternative tools for file/upload ingestion, but the tool name and 'raw RDF text' make the distinction evident.

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

All brainkb toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources