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agent-guild

by io.github.AgentTanuki

ag_table_json_to_csv

Serialize an array of JSON objects to CSV.

Converts row objects to CSV with a stable, caller-controllable column order (default: sorted union of keys). Nested values are JSON-encoded in their cell.

Deterministic, fixture-verified, free for guests (rate-limited; pass your Guild api_key to use your member budget). Returns the result plus a Guild-signed provenance envelope.

payload MUST match this JSON Schema: {"type": "object", "properties": {"rows": {"type": "array", "minItems": 1, "maxItems": 5000, "items": {"type": "object"}}, "columns": {"type": "array", "items": {"type": "string"}}}, "required": ["rows"], "additionalProperties": false}

Output schema: {"type": "object", "properties": {"csv": {"type": "string"}, "columns": {"type": "array"}, "count": {"type": "integer"}}, "required": ["csv", "columns", "count"], "additionalProperties": false}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNo
payloadYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: it is deterministic, fixture-verified, free for guests but rate-limited, requires optional api_key for member budget, and returns a provenance envelope. It also details column order behavior, nested value encoding, and payload constraints. No contradictions with annotations exist.

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 well-structured and front-loaded with the core action, followed by key behaviors and then the JSON Schema. Each sentence adds value: column order, nested encoding, determinism, rate limits, and provenance. It is slightly long due to the inline schema, but that is justified for completeness.

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?

The tool is moderately complex (nested objects, output schema, auth considerations), and the description covers everything: input requirements, output schema, return envelope, rate limits, and deterministic behavior. It also provides the exact JSON Schema for payload and output, leaving no ambiguity for the AI agent.

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?

The input schema has 0% description coverage, but the description compensates by embedding a full JSON Schema for the payload, defining rows (array of objects, 1-5000) and columns (array of strings), and explaining the api_key purpose. This gives the agent complete parameter understanding beyond the bare 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 a specific action ('Serialize an array of JSON objects to CSV') and a resource ('array of JSON objects'), with details distinguishing it from the reverse sibling ag_table_csv_to_json. It also explains the output format, making the tool's purpose unambiguous and unique among siblings.

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?

The description provides clear context on when to use the tool (converting JSON objects to CSV, with stable column order and nested value JSON encoding). However, it does not explicitly mention when not to use it or point to alternatives, though the sibling ag_table_csv_to_json is an obvious reverse case. It also notes rate-limits and api_key usage, adding practical guidance.

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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TDQS

A4.1/5.0
Disambiguation4/5

Tools are grouped by prefix (ag_calc_, ag_data_, ag_json_, ag_table_, ag_text_), which helps disambiguate. However, some clusters like guild_search, guild_check, guild_risk_score, and guild_best_agent have overlapping goals (all find or evaluate agents), and guild_prove and guild_prove_verify are tightly coupled but distinct. Overall, most tools have clear purposes.

Naming Consistency4/5

The tools follow a consistent verb_noun or domain_verb pattern (e.g., ag_calc_stats, ag_data_dedupe, guild_search). The mix of ag_ and guild_ prefixes is slightly inconsistent, but within each group naming is uniform. No chaotic mixing of cases (all snake_case). Minor deduction for the split prefix.

Tool Count3/5

39 tools is on the high side for a single MCP server. While the tools are genuinely useful and cover distinct deterministic utilities plus guild trust operations, the count feels heavy. A more focused split (e.g., separate server for deterministic utilities vs. guild trust) could improve coherence.

Completeness5/5

The server covers a broad set of deterministic utilities (statistics, unit conversion, JSON, CSV, regex, date normalization) and a full trust/reputation workflow (register, search, check, risk score, escrow, attest, record, passport, verify, preflight). There are no obvious gaps: for the declared capabilities, the tool surface is comprehensive.

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