"IBM MCP Context Forge - Model Context Protocol Framework" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Signed sandbox verdicts on any artifact, plus an agent marketplace. No key, no signup, no payment.
Multi-LLM entity enrichment: schemas, single/batch enrichment, fusion, model benchmarks.
GPU cloud pricing (H100, H200, B200 per hour) and AI model API pricing per 1M tokens, with history.
Compare AI model prices and estimate workload costs with four read-only tools. No account needed.
Compress prose for LLM context windows. Per-call USDC billing, signed receipt.
Persistent context and memory vault for AI. Save your brand, project facts, prompts, skills and sessions once, then use them in Claude, ChatGPT, Cursor and any MCP client.
Makes on-model clothing photos for online stores: pick a model and scene, shoot, approve frames.
Open AI models through one OpenAI-compatible API: chat, live model list, prices and cost estimates.
Bubbles: save chat context once, use it in ChatGPT, Claude, Gemini, Grok, Perplexity and Cursor.
Cloud-native, serverless Model Context Protocol (MCP) server using Streamable HTTP (https://brightcanoe.com/api/mcp) that enables AI agents like Claude and Cursor to check live calendar availability, create appointments, search Client 360 CRM records, and retrieve 24/7 AI voice receptionist call logs.
Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.
Verify before the agent pays. Screwtape is an independent check on agent work before a payment goes out: invoices, duplicate invoices and reconciliation, with a signed receipt for your auditor. It is independent of your agent's own model and vendor, with no stake in your agent passing. - judge: pass, fail or retry verdict on one agent output, with a score, reasons and a next_step. Optional deterministic checks: required fields, types, formats, sums, regex. - check_duplicate_invoice (/v1/duplicate): opt-in duplicate-invoice check using keyed fingerprints. - consensus: scores agreement across 2 to 16 agent answers and flags false consensus. - Advisory mode: recommends approve, review or return; a person decides. - Receipts: Ed25519-signed. The receipt proves what was checked, when, and the verdict. Verify via POST /v1/receipts/verify or offline; export via GET /v1/receipts/export. - Judged content isn't stored unless you opt in. Free key: 100 calls/day. Or $0.005 USDC per call via x402 on Base or Solana, no key needed. MCP: https://screwtape.ai/mcp Docs: https://screwtape.ai/docs/ Icon: https://screwtape.ai/icon-512.png Tags: verification, evaluation, llm-as-judge, invoices, receipts, x402
Famous writing voices for your agent. Find a voice, read how it was studied, and write in it with your own model.
Veqen brings together work and coding agent costs per person and team, product inference costs per customer, agent spending, protection incidents, invoice checks, chargeback and the month-end close. Ask your assistant to investigate recorded spending, compare model prices, review provider keys and incidents, record bills and credits, or manage budgets, alerts, teams and spending policies. Your account role and plan govern access. Provider-key shut-off and agent-card controls require the appropriate role and provider permissions. Destructive actions require confirmation, and organizations can require a second person to approve a shut-off. SDK budget blocking is not a guaranteed spending cap. Work and coding agents are free for up to 3 people; product inference is free up to $1,000 in monthly AI spend. Connect your Veqen account for private organization data. The separate public model-price server needs no account. Estimates remain separate from provider invoices. Setup and permissions: https://veqen-app.com/docs/mcp
Every EmpirioLabs model and platform feature as tools: chat, media, search, jobs, GPU Cloud, agents.
Paste a draft and see the habits that make writing read like it came from an AI model.
# **RChilli** RChilli MCP Hub is a production-grade Model Context Protocol (MCP) server that exposes RChilli’s HR data intelligence platform through AI-callable tools. Built on 15+ years of HR data intelligence, RChilli supports ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. The tools return consistent, structured JSON responses for recruiting, talent intelligence, skills, matching, compliance, and HR workflow use cases. `userkey` and `subuserid` are injected automatically from the Bearer token and must not be passed manually. <br> --- <br> # **Available Tools** <br> > ### **`analyze_hr_call`** > > Transcribes and analyzes HR or recruiting call recordings to extract speaker turns, sentiment, candidate intent, action items, skill mentions, and structured follow-up data for recruiting operations. <br> > ### **`analyze_skill_adjacency`** > > Identifies related, adjacent, and transferable skills to support skill discovery, talent mobility, workforce planning, and candidate evaluation. <br> > ### **`analyze_skill_gap`** > > Compares candidate or employee skills against a target job and returns present skills, missing skills, readiness score, and development areas for hiring, internal mobility, and workforce planning. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to standardized taxonomy profiles from the moment a recruiter starts typing. <br> > ### **`autocomplete_skill`** > > Accepts a partial skill string and returns ranked autocomplete suggestions with canonical skill names and categories. Helps prevent free-text entry errors and keeps skill data clean. <br> > ### **`classify_job_zone`** > > Reads the job profile from a resume or job description and returns its O*NET Job Zone, from Zone 1 through Zone 5, based on the education, experience, and training preparation required for the role. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Useful as a pre-processing step before extracting data from non-standard document formats. <br> > ### **`detect_bias`** > > Reviews job descriptions and hiring criteria for gendered language, age-coded terms, unnecessary credential inflation, cultural exclusion signals, and related compliance risks. Suggests neutral replacements. <br> > ### **`detect_resume_suspicion`** > > Reviews candidate profile data for possible inconsistencies, unusual claims, timeline conflicts, missing context, and other signals that require recruiter review. <br> > ### **`extract_contacts`** > > Identifies and structures names, emails, phone numbers, LinkedIn URLs, and addresses with field-level confidence scores from candidate records, emails, or documents. <br> > ### **`extract_job_data`** > > Extracts and converts job descriptions into structured hiring data including job title, required skills, preferred skills, responsibilities, experience, education, and taxonomy-normalized role requirements. <br> > ### **`extract_resume_data`** > > Extracts and converts resumes, CVs, and candidate documents into structured, searchable profiles with contact details, skills, experience, education, certifications, and taxonomy-enriched data for ATS, HCM, and AI recruiting workflows. Supports multilingual extraction, batch processing, resume auto-fill, and reprocessing of historical resumes. <br> > ### **`extract_resume_data_from_url`** > > Accepts a direct URL to a PDF, DOCX, or RTF file and returns the same normalized JSON profile as Resume Data Extraction. Ideal for resumes stored in cloud storage, S3, email attachments, or other URL-accessible sources. <br> > ### **`find_matches_in_index`** > > Accepts a resume or job description as input and returns the top matching documents from the indexed corpus, ranked by semantic similarity. <br> > ### **`generate_interview_questions`** > > Creates technical, behavioral, situational, and competency-based interview questions tailored to a role and candidate profile, including question type, competency, and expected answer signals. <br> > ### **`generate_jd`** > > Generates structured job descriptions with title, responsibilities, required skills, preferred skills, qualifications, tone alignment, language support, and inclusivity scoring from role inputs. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Supports radius-based candidate search and workforce planning. <br> > ### **`index_document`** > > Indexes a resume or job description in RChilli Search & Match so it can be searched, matched, updated, and reused across candidate matching, job matching, and similarity-search workflows. <br> > ### **`lookup_job_profile`** > > Returns authoritative detail for a known job profile including canonical title, SOC/O*NET code, job family, typical required and preferred skills, salary bands, and work context. <br> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, aliases, related skills, proficiency levels, and O*NET or ESCO mappings. <br> > ### **`map_job_to_skills`** > > Converts job descriptions into granular skill maps with required and preferred skill clusters, proficiency signals, taxonomy tags, and downstream matching attributes. <br> > ### **`match_end_to_end`** > > Orchestrates data extraction, indexing, taxonomy normalization, and matching to return ranked candidates or jobs with scores, explainable results, and an audit trail from a single call. <br> > ### **`normalize_candidate_data`** > > Cleans and normalizes candidate records from multiple ATS or HR systems, resolves field conflicts, deduplicates records, and produces standardized candidate data with a field-level change log. <br> > ### **`normalize_hr_data_with_lov`** > > Maps raw and inconsistent HR data values, including degrees, universities, certifications, job profiles, skills, and locations, to the closest configured List of Values entry with a standardized value and confidence score. <br> > ### **`recommend_learning_path`** > > Builds structured learning paths with prerequisites, skill sequence, certifications, proficiency targets, and LMS alignment based on a person’s profile and target role. <br> > ### **`redact_resume`** > > Redacts configured personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. <br> > ### **`reformat_resume_with_template`** > > Applies one of the RChilli resume templates, TM001 through TM006, to a structured candidate profile and produces a consistently formatted PDF, DOCX, RTF, or HTML document. <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one job description and returns an overall match score, dimension scores, matched skills, skill gaps, and a natural-language explanation. No index is required. <br> > ### **`screen_application_batch`** > > Evaluates a batch of candidates against job requirements and custom criteria, returning scores, insights, skill gaps, and recommended next actions for each candidate. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant’s indexed corpus. Supports Boolean and semantic search modes. <br> > ### **`skill_mapping`** > > Maps raw skill text to standardized RChilli taxonomy skills, aliases, classifications, and related skill information. <br> > ### **`tag_entities`** > > Annotates extracted HR text by wrapping recognized entities in structured XML-style labels. Supported entities include job titles, skills, names, degrees, organizations, locations, countries, dates, and years.
DevSpeak is a writing tool for developers, available to your coding agent over MCP. Call `optimize_text` to improve a PR description, commit message, ticket or doc in the language it was written in, or set `correctionsOnly` to fix only spelling, grammar and punctuation. Call `translate_text` to turn rough notes into a technical spec, Jira tickets, an RFC, an API design or a data model for the audience you choose (Junior Dev, Senior Dev, Tech Lead, SRE or Data Engineer). It also translates README files with `translate_markdown`, refines earlier output and lists your history. Requires a DevSpeak API key; a free account includes one key with 50 API requests a month.
Search open model file records, inspect recorded SHA-256 hashes and license labels, and check estimated memory fit. Public reads need no account. Connect over Streamable HTTP. Public board and request tools require user consent. A matching hash checks bytes; fit and license labels do not establish successful execution or permission for every use. Hosted MCP: https://huggingbay.xyz/api/mcp Install: npx -y hugging-bay-mcp@0.1.13 Docs: https://huggingbay.xyz/docs Repository: https://github.com/barneywohl/hugging-bay
Convert, lint and generate LLM tool schemas for OpenAI, Anthropic, Gemini and MCP
