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agent-guild

by io.github.AgentTanuki

ag_text_regex_extract

Bounded, safe regex extraction over text.

Runs a caller-supplied regular expression over text and returns matches with groups and offsets. Guarded: pattern length cap, nested-quantifier rejection, match-count cap — safe to expose to strangers.

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": {"text": {"type": "string", "maxLength": 60000}, "pattern": {"type": "string", "maxLength": 300}, "flags": {"type": "array", "items": {"enum": ["i", "m", "s"]}}, "max_matches": {"type": "integer", "minimum": 1, "maximum": 1000}}, "required": ["text", "pattern"], "additionalProperties": false}

Output schema: {"type": "object", "properties": {"matches": {"type": "array"}, "count": {"type": "integer"}, "truncated": {"type": "boolean"}}, "required": ["matches", "count", "truncated"], "additionalProperties": false}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNo
payloadYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 carries full burden and excels in transparency. It discloses safety guards (pattern length cap, nested-quantifier rejection, match-count cap), determinism, rate-limit information, and the Guild-signed provenance envelope, providing rich behavioral context.

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 front-loaded with a concise purpose statement, followed by essential safety and provenance details, then the payload and output schemas. Every sentence adds value with no fluff, and the structure is well-organized.

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?

For a regex extraction tool with safety considerations, the description is highly complete. It covers input structure, output structure, safety constraints, determinism, rate limits, and provenance, leaving minimal ambiguity for the 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 outer input schema simply has 'api_key' and 'payload' with no descriptions, but the description supplies the complete payload schema and explains key parameters like 'pattern' and 'text' indirectly. This greatly enhances the agent's ability to construct valid calls 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 identifies the tool as regex extraction over text, with a specific verb ('extract') and resource ('text'). It explicitly mentions returning matches with groups and offsets, distinguishing it from sibling tools like ag_json_path_extract.

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 for when to use the tool: any time regex extraction on text is needed. It does not explicitly mention alternatives or when-not-to-use, and there are no exclusions stated, but the specific regex scope and safety guards imply appropriate use cases.

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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